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Adam P. Herrmann,
Journal of Aerospace Information Systems, Volume 19, pp 70-82; https://doi.org/10.2514/1.i010992

Abstract:
This work explores on-board planning for the single spacecraft, multiple ground station Earth-observing satellite scheduling problem through artificial neural network function approximation of state–action value estimates generated by Monte Carlo tree search (MCTS). An extensive hyperparameter search is conducted for MCTS on the basis of performance, safety, and downlink opportunity utilization to determine the best hyperparameter combination for data generation. A hyperparameter search is also conducted on neural network architectures. The learned behavior of each network is explored, and each network architecture’s robustness to orbits and epochs outside of the training distributions is investigated. Furthermore, each algorithm is compared with a genetic algorithm, which serves to provide a baseline for optimality. MCTS is shown to compute near-optimal solutions in comparison to the genetic algorithm. The state–action value networks are shown to match or exceed the performance of MCTS in six orders of magnitude less execution time, showing promise for execution on board spacecraft.
Marc-Henri Bleu Laine, , Dimitri N. Mavris, Bryan Matthews
Journal of Aerospace Information Systems, Volume 19, pp 22-36; https://doi.org/10.2514/1.i010971

Abstract:
In recent years, there has been a rapid growth in applying machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen before the adverse event itself. Therefore, precursor mining provides many benefits, including the identification of factors relevant to the occurrence of an adverse event and their signatures, which can be tracked throughout a flight to alert the operators of the potential for an adverse event in the future. This work proposes using the multiple-instance learning framework, a weakly supervised learning task, combined with a carefully designed binary classifier leveraging a Multi-Head Convolutional Neural Network–Recurrent Neural Network (MHCNN-RNN) architecture. Multiclass classifiers are then created and compared, enabling the prediction of different adverse events for any given flight by combining binary classifiers, and by modifying the MHCNN-RNN to handle multiple outputs. Results obtained showed that the multiple binary classifiers perform better and are able to accurately forecast high-speed and high-path-angle events during the approach phase. Multiple binary classifiers are also capable of determining the aircraft parameters that are correlated to these events. The identified parameters can be considered precursors to the events and may be studied/tracked further to prevent these events in the future.
Utsav Saxena, Michael R. Dorothy, Imraan A. Faruque
Journal of Aerospace Information Systems, Volume 19, pp 53-61; https://doi.org/10.2514/1.i010951

Abstract:
Micro-aerial vehicles (MAVs) lack the endurance times demanded by typical mission applications, and previous work to minimize flight path disturbances has also quantified their high atmospheric sensitivities. This study introduces an approach to modulate the sensitivity through real-time parameter variation that translates into a net energy gain of the vehicle. By applying a control-theoretic disturbance sensitivity framework and observability gramian via a “gust capture metric,” the formulation modulates vehicle gust response to sensed favorable (or unfavorable) vertical gusts. The approach is implemented in a nonlinear simulation and an experimental test environment for a representative 21 g MAV. These results include the development of a system-identified MAV flight dynamics model and the development of an experimental facility to provide automated, repeatable indoor flight tests over a gust field. Nonlinear model simulation indicates that the control law provides altitude gain over an idealized gust field, consistent with theoretical analysis. The experimental flight facility was equipped with a gust field generation capability, and its velocity and turbulence intensity distribution was quantified. The experimental flight tests evaluating altitude gain under vehicle controller modification show agreement with theoretical and simulation results and indicate the importance of turbulence distribution in atmospheric gust harvesting experiments.
Cosme A. Ochoa,
Journal of Aerospace Information Systems, Volume 19, pp 37-52; https://doi.org/10.2514/1.i010979

Abstract:
Low-altitude urban flight planning for small unmanned aircraft systems (UASs) accurate vehicle kinodynamics, environment maps, and risk models to assure that flight plans consider the urban landscape as well as airspace constraints. This paper presents a suite of motion planning metrics designed for small UAS urban flight and defines map-based and path-based metrics to holistically characterize motion plan quality. Proposed metrics are examined in the context of representative geometric, graph-based, and sampling-based motion planners applied to a multicopter small UAS. A novel multi-objective heuristic is proposed and applied for graph-based and sampling motion planners at four urban UAS flight altitude layers. Monte Carlo case studies in a New York City urban environment illustrate metric map properties and planner performance. Motion plans are evaluated as a function of planning algorithm, location, range, and flight altitude.
Junghyun Kim, Cedric Justin, Dimitri Mavris, Simon Briceno
Journal of Aerospace Information Systems, Volume 19, pp 3-21; https://doi.org/10.2514/1.i010940

Abstract:
Airlines traditionally gather weather information before departure to generate flight routes that avoid hazardous weather while minimizing flight time. However, flight crews may have to perform in-flight replanning as weather information can significantly change after departure. This in-flight replanning activity is currently not fully automated, which has the potential to increase crew workload and adversely impact flight safety. The objective of this research is to mitigate some of these issues by developing an automated framework to perform continuous in-flight replanning. The proposed framework relies on three pillars and leverages: supervised machine learning technique to augment existing wind forecasts by providing a higher spatial and temporal granularity, unsupervised machine learning technique to perform short-term predictions of areas with significant convective activity, and graph-based pathfinding algorithm to generate optimized trajectories. The main contribution of this research is to combine these techniques to autonomously and continuously generate trajectories that minimize operating expenditures for airlines. Statistical analyses are performed to demonstrate the applicability and benefits of the proposed framework. Results indicate that optimized trajectories are 2% shorter than actual flight routes in most cases.
Jacob G. Elkins, Rohan Sood, Clemens Rumpf
Journal of Aerospace Information Systems, Volume 19, pp 62-69; https://doi.org/10.2514/1.i010958

Abstract:
Artificial intelligence is expected to revolutionize all areas of space operations in the coming years. The most advanced space systems will possess the ability to adapt and improve performance over time, or online learning. This work presents a novel framework that uses the highly researched artificial intelligence paradigm, reinforcement learning, to perform online learning. The spacecraft attitude control problem is used as a benchmark, with experimental results for using reinforcement learning to train neural spacecraft attitude controllers. Additionally, experimental results in a simulation environment are also shown to compare and contrast two state-of-the-art single-agent continuous control reinforcement learning algorithms to motivate their use in the online learning scenario.
Eugene Mangortey, Olivia Pinon Fischer, Dimitri N. Mavris
Journal of Aerospace Information Systems pp 1-13; https://doi.org/10.2514/1.i011030

Abstract:
Tremendous progress has been made over the last two decades toward modernizing the National Airspace System by way of technological advancements and the introduction of procedures and policies that have maintained the safety of the United States airspace. However, as with any other system, there is a need to continuously address evolving challenges pertaining to the sustainment and resiliency of the National Airspace System. One of these challenges involves efficiently analyzing and assessing daily airport operations for the identification of trends and patterns to better inform decision making so as to improve the efficiency and safety of airport operations. This research effort provides a repeatable methodology that leverages supervised and unsupervised machine learning techniques to categorize airports as a means to facilitate the analysis of their operations. In particular, it provides a means for stakeholders to assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations.
Edwin V. Odisho, , Robert E. Joslin
Journal of Aerospace Information Systems pp 1-15; https://doi.org/10.2514/1.i010972

Abstract:
Risk of runway excursion caused by pilots continuing an unstable approach to landing has been identified by aviation accident investigators as a primary contributing factor in airline landing accidents. The purpose of this research was to develop and test predictive models for unstable approach risk misperception in the National Airspace System using machine learning. The research applied machine learning algorithms to flight recorder data gathered from a fleet of commercial transport aircraft and made available by NASA. Once evidence of unstable approaches was identified and extracted from the flight recorder data, a determination was made whether a rejected landing or continuance to landing was made. Federal Aviation Administration unstable approach criteria were used in the identification of unstable approaches based on flight data variables evaluated at 500 feet above the ground on approach to landing. Six machine learning algorithms were used, including logistic regression, decision tree, gradient boosting, random forest, and support vector machine. The results indicated that the decision tree with three branches produced the best predictive model. This model was able to predict the pilot error of continuing an unstable approach to landing with an accuracy of 98%. Glideslope deviation, selected airspeed, localizer deviation, and flaps not extended were the most important influential predictors of pilot error leading to an unstable approach.
Guangrui Xie,
Journal of Aerospace Information Systems pp 1-11; https://doi.org/10.2514/1.i010997

Abstract:
As an important type of dynamic data-driven application system, unmanned aerial vehicles (UAVs) are widely used for civilian, commercial, and military applications across the globe. An increasing research effort has been devoted to trajectory prediction for non-cooperative UAVs to facilitate their collision avoidance and trajectory planning. Existing methods for UAV trajectory prediction typically suffer from two major drawbacks: inadequate uncertainty quantification of the impact of external factors (e.g., wind) and inability to perform online detection of abrupt flying pattern changes. This paper proposes a Gaussian process regression (GPR)-based trajectory prediction framework for UAVs featuring three novel components: 1) GPR with uniform confidence bounds for simultaneous predictive uncertainty quantification, 2) online trajectory change-point detection, and 3) adaptive training data pruning. The paper also demonstrates the superiority of the proposed framework to competing trajectory prediction methods via numerical studies using both simulation and real-world datasets.
Tom A. D. T. Rijndorp, , Coen C. de Visser, Olaf Stroosma, ,
Journal of Aerospace Information Systems, Volume 18, pp 933-948; https://doi.org/10.2514/1.i010895

Abstract:
Current aircraft flight deck interfaces do not provide information on how a performance-altering failure constrains an aircraft’s flight envelope. As a result, it is difficult for flight crews to plan maneuvers in order to reach navigation targets. This study presents the results of the conceptual development of constraint-based interface symbology that aims to address this issue. The proposed symbology is designed to integrate with both the primary flight display and navigation display. A small-scale, pilot-in-the-loop experiment (N=9 ) was conducted to assess the effectiveness of the used symbology in terms of flight performance and pilot usability. A simplified dynamic model with an asymmetric flight envelope was used to purposefully manipulate various levels of damage severity and corresponding flight envelopes. Results show that although the modifications to the primary flight display generally did not show statistically significant improvements, presenting flight envelope constraints as a reachable navigation envelope on the navigation display generally did do so for severe failures. The visualized envelope occasionally resulted in improved tactical control decisions at reduced workload levels. A future study involving a lager sample size and increased simulation realism should substantiate the discovered results.
Cory A. Seidel, Ethan Genter, David A. Peters
Journal of Aerospace Information Systems, Volume 18, pp 881-889; https://doi.org/10.2514/1.i010983

Abstract:
The use of finite-state methods is critical to the development of accurate and efficient inflow models used in rotorcraft flight dynamics simulation and control. Recent work in the finite-state field has allowed for the application of these models to multirotor systems using the adjoint theorem, which involves time delays and adjoint variables. However, the addition of time delays and adjoint variables drives the necessity for the addition of further inflow states to achieve model accuracy. Computation with a higher numbers of inflow states requires greater computing power and therefore limits the ability of real-time analysis. To help mitigate these issues, this paper explores the use of a gradient booted trees in XGBoostTM, as well as the use of varied, lower state training data and limited higher state training data, to accurately predict the velocity on the lower rotor of a coaxial rotor helicopter. The investigation involves XGBoost hyperparameter searches to determine the best model, variation in training and testing subset splits, and use of validation subset comparisons for identifying the best performing model.
Kyungwoo Hong, Sungjoong Kim, Junwoo Park, Hyochoong Bang
Journal of Aerospace Information Systems, Volume 18, pp 964-972; https://doi.org/10.2514/1.i010957

Abstract:
This study proposes a novel approach for a vision-based navigation problem using semantically segmented aerial images generated by a convolutional neural network. Vision-based navigation provides a position solution by matching an aerial image to a georeferenced database, and it has been increasingly studied for global navigation satellite system–denied environments. Aerial images include a vast amount of information that infers the position where they are located. However, it also includes features that disturb the estimation accuracy. The progress of convolutional neural network may provide a promising solution for extracting only helpful features for this purpose. Therefore, segmented images are modeled as a Gaussian mixture model, and the L2 distance for a quantitative discrepancy between two images is established. This allows us to compare the two images quickly with improved accuracy. In addition, a framework of a particle filter is applied to estimate the position using an inertial navigation system. It employs the L2 distance as a measurement, and the particles tend to converge to the true position. Flight test experiments were conducted to verify that the proposed approach achieved distance error of less than 10 m.
Majeed Mohamed, Nidhin Joy
Journal of Aerospace Information Systems, Volume 18, pp 949-963; https://doi.org/10.2514/1.i010894

Abstract:
Calibration of aircraft airdata system deals with the reconstruction of flight path trajectories from the noisy flight data using six-degree-of-freedom equations of aircraft. Aircraft system dynamics are highly nonlinear in rapid variations of the aircraft motion and require the use of a nonlinear filtering algorithm. In this paper, a methodology based on data-driven decision making is introduced to obtain the accurate values of aircraft flow angles (angle of attack and angle of sideslip) and static pressure from its noisy measurements. For this, the integration of fault detection and isolation approach to the adaptive nonlinear filter is applied to dynamic maneuvers, and a neural model of calibration function is established over a flight envelope using the filter estimates. A deterministic airdata calibration function is derived by estimating its coefficients from the established neural model using the neural partial differentiation method. The cascading impact of adaptive estimation and neural modeling of airdata calibration function reduces the development cost of an aircraft. The investigations are initially made on simulated flight data under various conditions of wind and turbulence and later extended to the flight data of aircraft to identify the calibration function valid over a flight envelope. The complementary flight data are used to validate the calibration function and are compared with online estimation results of the robust filter. The experimental results show that the proposed algorithm can isolate and rectify the fault and exhibits more accurate estimates directly with neural modeling than the nonadaptive version of the filter.
, Ludovic Apvrille, Rob Vingerhoeds
Journal of Aerospace Information Systems, Volume 18, pp 906-918; https://doi.org/10.2514/1.i010950

Abstract:
Systems engineering, or engineering in general, has long been relying on document-centric approaches. Switching to model-based systems engineering, or MBSE for short, has extensively been discussed over the past three decades. Since about two decades, MBSE has been commonly associated with the modeling language SysML (Systems Modeling Language), which offers a standardized notation, not a methodology of using it. SysML needs therefore to be associated with a methodology supported by tools. In this paper, a methodology supported by the free and open-source software TTool is associated with SysML. This paper focuses discussion on methodological issues, leading the authors to share their experience in real-time systems modeling. Modeling with SysML is more than just drawing the different diagrams. Associated tools offer possibilities to analyze SysML models for specific properties. In this paper, verification addresses both safety and security properties. The TTool model checker inputs the SysML model enriched with safety properties to be verified and outputs an yes/no answer for each property. Security verification checks SysML models against confidentiality, integrity, and authenticity properties. As an illustration of the proposed approach, an aircraft cockpit door control system is modeled in SysML and verified against safety and security properties.
Marc W. Brittain, Xuxi Yang, Peng Wei
Journal of Aerospace Information Systems, Volume 18, pp 890-905; https://doi.org/10.2514/1.i010973

Abstract:
A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynamic en route sector. The concept of using distributed vehicle autonomy to ensure separation is proposed, instead of a centralized sector air traffic controller. Our proposed framework uses proximal policy optimization that is customized to incorporate an attention network. This allows the agents to have access to variable aircraft information in the sector in a scalable, efficient approach to achieve high traffic throughput under uncertainty. Agents are trained using a centralized learning, decentralized execution scheme where one neural network is learned and shared by all agents. The proposed framework is validated on three case studies in the BlueSky air traffic simulator. Several baselines are introduced, and the numerical results show that the proposed framework significantly reduces offline training time, increases safe separation performance, and results in a more efficient policy.
Hassan Haghighi, , Daniel Delahaye
Journal of Aerospace Information Systems, Volume 18, pp 919-932; https://doi.org/10.2514/1.i010866

Abstract:
This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the three-dimensional mountainous environment. As a contribution to solving tradeoffs in the optimization problem, revisit time is conjugated with other contrary costs effective in flight planning through Pareto analysis. By introducing the revisit time and applying a specific setup to mitigate computational complexity, the proposed algorithm efficiently revisits the desired zones, which are more important to be revisited during the patrolling mission. The results of the introduced modified A* algorithm are compared in various scenarios with two different algorithms: a complete and optimal algorithm known as Dijkstra, and an evolutionary algorithm known as the genetic algorithm. Simulation results demonstrate that the proposed algorithm generates faster and more efficient trajectories in complex multi-agent scenarios due to the introduced cell selection method and dynamic-based simplifications applied in this research.
Hyungjoo Ahn, Junwoo Park, Hyochoong Bang, Yoonsoo Kim
Journal of Aerospace Information Systems pp 1-15; https://doi.org/10.2514/1.i010956

Abstract:
This study proposes a collision-free motion planning framework for indoor autonomous flight of multirotor unmanned aerial vehicle (UAV) based on the convex model predictive control (MPC) approach under a three-dimensional point cloud environment. The suggested framework is divided into three steps: full reference path generation, piecewise flight corridor (PFC) creation, and MPC-based motion planning. The framework begins with reconstructing boundary surfaces that can encapsulate the given point cloud in order to generate a full reference path by applying Dijkstra and Voronoi diagram algorithms. Then PFC that represents locally convex and feasible flight corridor is generated using the current vehicle state, triangulized obstacle, and full reference path. In such a way, the entire problem breaks down into a series of discretized convex motion planning problems whose solution can be found by applying MPC iteratively until the UAV reaches its final destination. The constraints of the MPC are set up with the dynamics of the UAV, PFC, and the performance limitation of the platform. The framework is verified with simulation under a MATLAB environment. As a result, the UAV can find the control variable needed to reach the final destination with the suggested framework. Also, the computational time of the suggested framework is shorter than those of full reference path optimization methods.
HanSeok Ryu, Changho Lee, Youngmin Park, Matthew B. Rhudy, Dongjin Lee, Dongjin Jang, Wonkeun Youn
Journal of Aerospace Information Systems pp 1-13; https://doi.org/10.2514/1.i010967

Abstract:
Identifying the drag parameters of unmanned aerial vehicles (UAVs) is crucial for guaranteeing their aerodynamic efficiency. However, in contrast to commercial aircraft, obtaining accurate UAV drag parameters is challenging because the existing approaches rely on analytical models or require accurate modeling of the engine thrust, which highly depends on time-varying wind conditions. To address this challenge, this paper first proposes a novel in-flight estimation algorithm for the air data (airspeed, angle of attack, and sideslip angle) and drag parameters of UAVs. With this approach, there is no need to compute all of the contributing components for drag, to model the thrust of the UAVs, or to perform complicated wind tunnel testing/computational fluid dynamics analysis to obtain the drag parameter. Instead, the proposed algorithm requires only standard sensors such as inertial measurement units and air data systems during gliding flight. Then, an efficient glide phase detection algorithm for initiating the filter is proposed. Simulation and experimental flight testing of a UAV demonstrate that the proposed algorithm yields accurate zero lift drag coefficient, attitude, and air data estimation results according to thorough validation with newly derived metrics for performance evaluation.
Aneesh M. Heintz, Mason Peck, Fangchen Sun, Ian Mackey
Journal of Aerospace Information Systems pp 1-12; https://doi.org/10.2514/1.i011014

Abstract:
Neural networks have become state-of-the-art computer vision tools for tasks that learn implicit representations of geometrical scenes. This paper proposes a two-part network architecture that exploits a view-synthesis network to understand a context scene and a graph convolutional network to generate a shape body model of an object within the field of view of a spacecraft’s optical navigation sensors. Once the first part of the network’s architecture understands the spacecraft’s environment, it can generate images from novel observations. The second part uses a multiview set of images to construct a 3D graph-based representation of the object. The proposed network pipeline produces shape models with accuracies that compete with state-of-the-art methods currently used for missions to small bodies. The network pipeline can be trained for multi-environment missions. Moreover, the onboard implementation may be more cost-effective than the current state-of-the-art.
Chuanqi Zhang, Yunfeng Cao, Meng Ding, Xu Li
Journal of Aerospace Information Systems pp 1-10; https://doi.org/10.2514/1.i011022

Abstract:
The flight safety of low-altitude small fixed-wing unmanned aerial vehicles (UAVs) is often threatened by obstacles such as buildings. This requires UAVs to have the ability to autonomously measure the depth of objects ahead. However, existing depth measurement methods based on multiview geometry and handcrafted features still have problems in accuracy and scene suitability. This paper proposes an object depth measurement and filtering method for UAVs by using monocular images. Firstly, the length of the line segment between feature points instead of the pixel position of feature point is used to solve object depth, which reduces the adverse effect of feature matching error. Meanwhile, in order to adapt to UAV platforms, height and attitude changes are both considered in the modeling process. Moreover, the sequence of object depth values corresponding to the image sequence is filtered by an extended Kalman filter to reduce oscillations. The effectiveness of the whole scheme is verified by visual simulation. Results show that the proposed method achieves better accuracy than other depth measurement methods based on multiview geometry.
Toshikazu Motoda
Journal of Aerospace Information Systems pp 1-11; https://doi.org/10.2514/1.i010926

Abstract:
Monte Carlo simulation (MCS) has been widely used in the development of aerospace vehicles. The rapid growth of computer power has allowed it to be applied in particular to the evaluation of systems before flight, where its capability of directly evaluating nonlinear systems incorporating various uncertain inputs is a key advantage. To improve a system’s design, it is crucial to detect those uncertain inputs that have a significant influence on unsatisfactory MCS results. This paper presents a new approach for detecting such input parameters quickly. It combines two statistical tests: Kuiper’s test and the Z test. The advantage of the approach is that it uses only input and output data from MCS simulation evaluations: knowledge of the system model or further simulations are unnecessary. Because its results can be obtained quickly, the method contributes to the efficient development of flight vehicles. The approach also can be applied to MCS results that have a relatively small number of unsatisfactory results, so it is effective not only during the early stages of design where failures are more likely, but also for successive design iterations where failures are fewer.
Haichao Li, Linwei Qiu, Zhi Li, Bo Meng, Jianbin Huang, Zhimin Zhang
Journal of Aerospace Information Systems, Volume 18, pp 755-761; https://doi.org/10.2514/1.i010925

Abstract:
Accurate detection and segmentation of obstacles is the key to the smooth operation of the planetary rovers and the basic guarantee of scientific exploration mission. The traditional method of rock segmentation based on boundary detector is affected by the change of illumination and dust storms. To address this problem, this paper proposes an improved U-net-based architecture combined with Visual Geometry Group (VGG) and dilated convolutional neural network for the segmentation of rocks from images of planetary exploration rovers. The proposed method also has a contracting path and an expansive path to get high-resolution output similar with U-Net. In the contracting path, the convolution layers in U-Net are replaced by the convolutional layers of VGG16. Inspired by the dilated convolution, the multiscale dilated convolution in the expansive path is proposed. Furthermore, our method is further optimized in the expansive path. To evaluate the proposed method, extensive experiments on segmentation with the Mars dataset have been conducted. The experimental results demonstrate that the proposed method produces accurate semantic segmentation and identification results automatically and outperforms state-of-the-art methods.
Hyeok-Joo Chae, Han-Lim Choi
Journal of Aerospace Information Systems, Volume 18, pp 790-802; https://doi.org/10.2514/1.i010968

Abstract:
Although deep learning techniques have been successfully implemented to solve domain-specific unmanned aerial vehicle planning problems, it is still a challenging task to develop a learning method to solve multidomain planning problems. Because the multidomain problems often involve learning more parameters, a dilated dataset diminishes learning speed due to its size and high dimensionality. The following two observations help tackle the issue: the state space of planning problems can be decomposed into representations of the domain state and system state, and the dimensionality problem often arises due to the huge size of the domain rather than system state. Inspired by such observations, this work presents a learning framework consisting of two networks: 1) a domain abstraction network in the form of a variational autoencoder that reduces the dimension of the domain space into a compact form, and 2) a planning network that generates a planning solution for a given domain setting. The effectiveness of the proposed learning framework is validated in case studies.
Chester V. Dolph, Cyrus Minwalla, Louis J. Glaab, Michael J. Logan, B. Danette Allen, Khan M. Iftekharuddin
Journal of Aerospace Information Systems, Volume 18, pp 838-851; https://doi.org/10.2514/1.i010911

Abstract:
Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation in autonomous small unmanned aerial system (sUAS) operations. Machine vision systems, based on standard optics and visible light detectors, possess the ideal size, weight, and power (SWaP) requirements for sUAS. This work presents the design and analysis of a novel aircraft detection and tracking pipeline based on optical sensing alone. Key contributions of the work include a refined range inequality model based on sensing and detection with Federal Aviation Administration well-clear separation assurance distances between aircraft in mind, a detector fusion method to maximize the benefit of two image detectors, and a comparative analysis of linear Kalman filtering and extended Kalman filtering to seek optimal tracking performance. The pipeline is evaluated offline against multiple intruder platforms, using two types of flight encounters: multirotor sUAS versus fixed-wing sUAS and multirotor sUAS versus general aviation (GA) plane. Analysis is restricted to the rate-limiting head-on and departing collision volume cases vertically separated for safety. Results indicate that it is feasible to use the proposed optical spatial-temporal tracking algorithm to provide adequate alerting time to prevent penetration of well-clear separation volumes for both sUAS and GA aircraft.
Song-Chen Han, Bi-Hao Zhang, Wei Li, Zhao-Huan Zhan
Journal of Aerospace Information Systems, Volume 18, pp 852-859; https://doi.org/10.2514/1.i010902

Abstract:
This paper presents a novel method for localization and recognition of moving objects in a real airport surface scene. Different from the traditional applications, moving object detection (MOD) in the airport surface is more challenging because the background is an open outdoor environment, which means that the target objects are usually low in resolution and the MOD task is vulnerable to many undesired changes, such as cloud movement and illumination variations. To address these issues, this paper proposes a unified and effective deep-learning-based MOD architecture, which combines both appearance and motion cues. Specifically, a novel moving region proposal generation module is first designed, which can effectively locate the regions of moving object based on the motion information. Meanwhile, a novel cascade multilayer feature fusion module with transposed convolution is applied to produce both enriched-semantics and fine-resolution convolutional feature maps for category recognition. Finally, a large-scale dataset acquired by the daily surveillance videos of a real airport surface is manually constructed. Results show that the proposed methods outperform state-of-the-art solutions in extracting moving objects from airport surface scenes.
Rohan S. Sharma, Serhat Hosder
Journal of Aerospace Information Systems, Volume 18, pp 774-789; https://doi.org/10.2514/1.i010966

Abstract:
The goal of this work was to investigate the feasibility of developing machine learning models for predicting the values of aircraft configuration design variables when provided with time series of mission-informed performance parameters. Regression artificial neural networks, along with their associated training data, have been generated and tested for aircraft design space exploration scenarios. The bounds of the data used to train the models were partially informed by the configuration characteristics of the Boeing 737 Next Generation family. The effects of varying neural network architecture, along with the application of different data filtering schemes, on the models’ predictive accuracy have been examined. The results obtained demonstrated that cascade-forward shallow neural networks not only exhibited excellent generalization across the design space for which the model was calibrated for, but also showcased versatility when tasked with predicting design variable values for a configuration layout relatively different from the ones used for training. Furthermore, these models had favorable metrics in computational wall-clock time required and number of epochs needed for training.
Zhengyi Wang, Daniel Delahaye, Jean-Loup Farges, Sameer Alam
Journal of Aerospace Information Systems, Volume 18, pp 860-875; https://doi.org/10.2514/1.i010954

Abstract:
In high-density urban air mobility (UAM) operations, mitigating congestion and reducing structural constraints are key challenges. Pioneering urban airspace design projects expect the air vehicles to fit into structured UAM corridor networks. However, most existing air transport networks are not capable of handling the increasing traffic demand, which is likely to cause congestion, traffic complexity, and safety issues. To adapt the increasing demand to the current airspace capacity, a novel macroscopic traffic assignment model is proposed to mitigate the congestion and organize the structure of air traffic flow. Firstly, the UAM corridor is designed and fitted into graph representation. Then, a traffic assignment problem based on linear dynamic system is formalized to minimize the congestion factors and the intrinsic air traffic complexity. A two-step resolution method based on Dafermos’s algorithm is introduced to efficiently solve this optimization problem. A case study is carried out on a two-layer air transport network with intensive UAM operations. The results demonstrate that the proposed model can successfully mitigate urban airspace congestion and organize the UAM traffic into a low-complexity flow pattern. This approach can be used as a tool to assist air navigation service provider in strategic planning for a given transportation network.
Julien Pelamatti, Loïc Brevault, Mathieu Balesdent, El-Ghazali Talbi, Yannick Guerin
Journal of Aerospace Information Systems, Volume 18, pp 813-837; https://doi.org/10.2514/1.i010965

Abstract:
Within the framework of complex system analyses, such as aircraft and launch vehicles, the presence of computationally intensive models (e.g., finite element models and multidisciplinary analyses) coupled with the dependence on discrete and unordered technological design choices results in challenging modeling problems. In this paper, the use of Gaussian process surrogate modeling of mixed continuous/discrete functions and the associated challenges are extensively discussed. A unifying formalism is proposed in order to facilitate the description and comparison between the existing covariance kernels allowing to adapt Gaussian processes to the presence of discrete unordered variables. Furthermore, the modeling performances of these various kernels are tested and compared on a set of analytical and aerospace-engineering-design-related benchmarks with different characteristics and parameterizations. Eventually, general tendencies and recommendations for such types of modeling problem using Gaussian process are highlighted.
Kevin Albarado, Leonardo Coduti, Diane Aloisio, Stephen Robinson, Daron Drown, Dan Javorsek
Journal of Aerospace Information Systems pp 1-11; https://doi.org/10.2514/1.i010991

Abstract:
Warfare is increasing in complexity, speed, and scale—not only due to enhanced technological capabilities but also from the employment methodologies associated with them. Incorporating artificial intelligence (AI) technology into this realm is a cogent solution to help address these complications because of the reduced cost, reduced risk to human life, and increased capability to rapidly adapt to changing environments. However, the introduction of AI comes with a host of new considerations. If AI is to be successfully integrated into air combat, humans must be included in the AI processing loop, and human interaction with AI decision loops must be frictionless. Additionally, AI-supported battle management systems must be designed for high and increasing human trust across dynamically changing scenarios. This paper presents AlphaMosaic, an AI battle manager developed as part of the Defense Advanced Research Projects Agency Air Combat Evolution program that is designed to incorporate human feedback in a manner conducive to true manned–unmanned aircraft teaming in beyond visual range air-combat scenarios.
Journal of Aerospace Information Systems, Volume 18, pp 728-738; https://doi.org/10.2514/1.i010922

Abstract:
The reconstruction of atmospheric properties encountered during Mars entry trajectories is a crucial element of postflight mission analysis. This paper proposes a deep learning architecture using a long short-term memory (LSTM) network for the reconstruction of Martian density and wind profiles from inertial measurements and guidance commands. The LSTM is trained on a large set of Mars entry trajectories controlled through the fully numerical predictor-corrector entry guidance (FNPEG) algorithm, with density and wind from the Mars Global Reference Atmospheric Model (GRAM) 2010. The training of the network is examined, ensuring that the LSTM generalizes well to samples not present in the training set, and the performance of the network is assessed on a separate training set. The errors of the reconstructed density and wind profiles are, respectively, within 0.54 and 1.9%. Larger wind errors take place at high altitudes due to the decreased sensitivity of the trajectory in regions of low dynamic pressure. The LSTM architecture reliably reproduces the atmospheric density and wind encountered during descent.
Min Xue, Melissa Wei
Journal of Aerospace Information Systems, Volume 18, pp 702-710; https://doi.org/10.2514/1.i010939

Abstract:
This work proposes a fast algorithm for generating obstacle-free and wind-efficient flight paths at a constant above-ground-level altitude in urban environments because a fast flight path planning algorithm is an essential function or service needed for enabling small unmanned aerial vehicle (sUAV) to operate in urban environments within Class G airspace. The proposed method first converts the 3D path planning problem to a 2D problem by constructing an obstacle map at a given above-ground-level altitude. A quad-tree decomposition is then used to build a search space in terms of obstacle occupancy and wind difference. The wind cost of traveling through each cell is defined based on energy consumption under various wind conditions. A repulsive potential is also adopted to make sure that the flight plans stay away from obstacles. The Theta* search algorithm, a variant of A* algorithm, is applied to mitigate the path angle change constraints introduced by grid-based graphs. With the Theta* and postsmoothing techniques, an obstacle-free, wind efficient, and constant above-ground-level flight plan can be quickly generated for sUAV operations in urban environments while meeting the lateral path angle constraints. The results showed that the path planning algorithm is efficient and can be finished within several seconds. With a proper choice of wind coefficient, the proposed path planning algorithm outperforms the multiple-shooting trajectory optimization method even in an obstacle-free environment. With the flexibility of incorporating other geo-related costs and computation efficiency, the proposed algorithm shows the potential for real-time flight path planning in complex urban environments.
Marc D. Takahashi, Brian T. Fujizawa, Jeffery A. Lusardi, Matthew S. Whalley, Chad L. Goerzen, Gregory J. Schulein, Nathan L. Mielcarek, Mark J. Cleary, James P. Carr, David W. Waldman
Journal of Aerospace Information Systems, Volume 18, pp 686-701; https://doi.org/10.2514/1.i010880

Abstract:
An autonomous guidance and flight control system was integrated and flight tested on a Black Hawk helicopter as part of an effort to provide all weather capability for U.S. Army fleet rotorcraft. The guidance and flight-control system components were previously flight tested on a full-authority helicopter, and were adapted to fly on the partial-authority test helicopter of this paper. The main autonomy guidance components consisted of the Risk Minimizing Obstacle Field Navigation algorithm, Safe Landing Area Determination algorithm, Mission Manager, and Integrated Cueing Environment. These components provided reactive obstacle avoidance guidance, landing site selection, pilot/autonomy interaction, and pilot situational awareness for degraded visual environments. In addition to the autonomy components, the Autonomous Partial-Authority Flight Control System provided a fully stabilized path-following capability to the guidance components. This paper describes how these components were adapted to a partial-authority helicopter that is typical of the current U.S. Army fleet. To test the system, a laser detection and ranging unit was used as a surrogate ranging device in lieu of an all-weather sensor system that was concurrently under development. Flight test results are presented from the fully integrated system navigating through terrain, selecting landing sites, and autonomously landing, while simultaneously keeping the pilot situationally aware of the autonomy’s intent.
, Arpan Biswas, Christopher Hoyle, Irem Y. Tumer, Chetan Kulkarni, Kai Goebel
Journal of Aerospace Information Systems, Volume 18, pp 665-678; https://doi.org/10.2514/1.i010942

Abstract:
To achieve system resilience, one can leverage high-level design features (e.g., redundancies and fail-safes), adjust operational profiles (e.g., load or trajectory), and use appropriate contingency management (e.g., emergency procedures) to mitigate potential hazards. For example, in the design of a novel drone, one would optimize the rotor and battery pack architectures (design), flight-plan (operations), and flight reconfiguration plans (contingency management) to maximize operational value while minimizing failure risk. In this work, the integrated resilience optimization formulation of the resilient design problem is defined, in which the system design, operational profile, and contingency management are optimized in a single framework. To understand how best to leverage this framework in early design exploration, sequential, all-in-one, and bilevel optimization architectures on the exhaustive search of a discrete-variable drone model are then compared in terms of their effectiveness and computational performance. This comparison shows that using a bilevel or all-in-one optimization architecture can lead to better solutions than sequential architectures in design problems where the levels are coupled. Additionally, for this problem, a bilevel structure has lower computational cost than the all-in-one architecture, especially when the lower-level resilience optimization problem is decomposed into independent subproblems for each set of fault modes.
Burak Yuksek, Mustafa Umut Demirezen, ,
Journal of Aerospace Information Systems, Volume 18, pp 739-750; https://doi.org/10.2514/1.i010961

Abstract:
In this study, reinforcement learning (RL)-based centralized path planning is performed for an unmanned combat aerial vehicle (UCAV) fleet in a human-made hostile environment. The proposed method provides a novel approach in which closing speed and approximate time-to-go terms are used in the reward function to obtain cooperative motion while ensuring no-fly-zones (NFZs) and time-of-arrival constraints. Proximal policy optimization (PPO) algorithm is used in the training phase of the RL agent. System performance is evaluated in two different cases. In case 1, the warfare environment contains only the target area, and simultaneous arrival is desired to obtain the saturated attack effect. In case 2, the warfare environment contains NFZs in addition to the target area and the standard saturated attack and collision avoidance requirements. Particle swarm optimization (PSO)-based cooperative path planning algorithm is implemented as the baseline method, and it is compared with the proposed algorithm in terms of execution time and developed performance metrics. Monte Carlo simulation studies are performed to evaluate the system performance. According to the simulation results, the proposed system is able to generate feasible flight paths in real-time while considering the physical and operational constraints such as acceleration limits, NFZ restrictions, simultaneous arrival, and collision avoidance requirements. In that respect, the approach provides a novel and computationally efficient method for solving the large-scale cooperative path planning for UCAV fleets.
R. Mori
Journal of Aerospace Information Systems, Volume 18, pp 679-685; https://doi.org/10.2514/1.i010941

Abstract:
The stabilized approach is important to avoid aircraft accidents during landing. Although there are many possible factors that can lead to an unstable approach, actions can be taken to mitigate the risk if the reason for unstable approaches is identified properly. However, because unstable approaches are rarely observed, it is difficult to identify the relevant reasons by analyzing unstable approaches only. Instead, the author proposes to identify untypical flights, which are not necessarily categorized into unstable approaches, by using neural network. Using the proposed method, more flights are available to identify the reason of unstable approaches. The stability index is estimated by neural network considering the current flight conditions such as wind and initial deviation. Therefore, untypical flights can be detected by comparing the actual stability index and the estimated stability index. The long-term interaction between the stability index and flight conditions is modeled by using gated recurrent unit. As a result of modeling, some of flights are categorized into untypical flights, and the false localizer beam is observed for most of these flights, which could be a potential hazard for unstable approaches. The proposed approach has a big potential to identify such potential hazards based on historical flight data.
, Gregg Rabideau, Daniel Q. Tran, Martina Troesch, Federico Nespoli, Miguel Perez Ayucar, Marc Costa Sitja, Claire Vallat, Bernhard Geiger, Fran Vallejo, et al.
Journal of Aerospace Information Systems, Volume 18, pp 711-727; https://doi.org/10.2514/1.i010899

Abstract:
Rosetta was a European Space Agency (ESA) cornerstone mission that launched in March 2004, exited hibernation in January 2014, entered orbit around the comet 67P/Churyumov-Gerasimenko in August 2014, and escorted the comet through September 2016, executing the most detailed study of a comet ever undertaken by humankind. The Rosetta Orbiter had 11 scientific instruments (4 remote sensing) and the Philae Lander to make complementary measurements of the comet nucleus, coma (gas and dust), and surrounding environment. The ESA Rosetta Science Ground Segment (SGS) used a science scheduling system that included an automated scheduling capability to assist in developing science plans for the Rosetta Orbiter. While the automated scheduling was a small portion of the overall SGS as well as the overall scheduling system, this paper focuses on the automated and semi-automated scheduling software (called Automated Scheduling and Planning ENvironment - Rosetta Science Scheduling Component (ASPEN-RSSC)) discussing: 1) the scheduling and constraint checking capabilities of ASPEN-RSSC; and 2) how the software was used pre-exit from hibernation, prelander delivery, and escort phase of the mission; 3) challenges in using the software and lessons learned for future use of automated scheduling technology for future space missions.
, Bryan Matthews, Thomas Templin
Journal of Aerospace Information Systems pp 1-15; https://doi.org/10.2514/1.i010959

Abstract:
The identification of precursors to safety incidents in aviation data is a crucial task, yet extremely challenging. The main approach in practice leverages domain expertise to define expected tolerances in system behavior and flags exceedances from such safety margins. However, this approach is incapable of identifying unknown risks and vulnerabilities. Various machine-learning approaches have been investigated and deployed to identify anomalies, with the great challenge of procuring enough labeled data to achieve reliable and accurate performance. This paper presents an explainable deep semi-supervised model for anomaly detection in aviation, building upon recent advancements described in the machine-learning literature. The proposed model combines feature engineering and classification in feature space, while leveraging all available data (labeled and unlabeled). Our approach is validated with case studies of anomaly detection during the takeoff and landing phases of commercial aircraft. Our model outperforms the state-of-the-art supervised anomaly-detection model, reaching significantly higher accuracy and fewer false alarms, even if only small proportion of data in the training set is labeled.
, Zheng Jian, Xili Wan, Renrui Xiao, Yifeng Li
Journal of Aerospace Information Systems pp 1-10; https://doi.org/10.2514/1.i010960

Abstract:
It is vital and essential to accurately and timely detect various damages of aircraft engines in civil aviation. Currently, aircraft engines are manually inspected via borescope images by aircraft maintenance technicians. This process is time-consuming and prone to error due to human factors. The aim of this paper is to automate the aircraft engine inspection, and this work presents a deep learning framework with a context encoder neural network structure such that the damaged structures can be accurately segmented from borescope images. Moreover, the proposed network structure is further optimized through an orthogonal-array-based method. With the real borescope images collected from a commercial airline company, the proposed framework is compared with existing deep-learning-based methods from various aspects. The experimental results validate that various damages can be automatically detected and recognized with high accuracy and efficiency by the proposed solution.
Mitchell Kirshner,
Journal of Aerospace Information Systems pp 1-9; https://doi.org/10.2514/1.i010986

Abstract:
With technological advances in the 21st century and the rise of the commercial space industry, crewed Mars missions to land humans on our red neighbor have become increasingly feasible. One of the key challenges in accomplishing this goal is determining whether model-based systems engineering training is necessary for space systems engineers to adopt digital engineering. This paper reviews model-based systems engineering benefits and limitations, as well as historically significant crewed Mars missions that planned to land on the surface. Models are presented for a Mars transit habitat developed in the modeling language SysML using Cameo Systems Modeler. These models leverage Cameo’s simulation capabilities to execute MATLAB scripts integrating digital engineering software Systems Tool Kit into the modeling process, demonstrating a reusable interoperability methodology for Mars mission planning not yet existing in this domain’s literature. It can be concluded that integrating model-based systems engineering with digital engineering for crewed Mars landing mission planning can lead to multiple benefits for space systems digital engineering, including 1) greater system simulation capabilities, 2) enhanced model fidelity, and 3) improved system understanding. Future research to further enhance these benefits includes adding cybersecurity considerations and greater system detail to the models.
, Sihan Liu, Huiying Li
Journal of Aerospace Information Systems pp 1-19; https://doi.org/10.2514/1.i010982

Abstract:
Aircraft taxiing in large airports is often delayed by traffic conflicts. The increased fuel consumption and pollutant emission lower the efficiency of airport surface operation and bring potential safety hazards. Previous studies on route planning for surface taxiing seldom involve refined delay analysis under different traffic conflict types and discussions on route planning at airports with various environmental parameters and under diverse aircraft types. In this study, dynamic models of crossing, head-on, and trailing conflicts during aircraft taxiing were constructed, and delays of these three conflict types were determined. The shortest route set of restricted routing was determined by the Yen algorithm according to the topological network, conflict-induced delay, and spatial distributions of taxiing at airports. Considering the three optimization objects, the aircraft route planning was optimized and determined from the shortest route set according to the differences in the environmental parameters and aircraft types. Experiment results show that the proposed method achieves 9.6–12.1% higher route planning precision in all traffic periods compared with previous route planning with considerations to taxiing conflict. This method also provides support to the dynamic decision of airport operation and control departments according to the environment and performance of aircraft.
Antony Gillette, Alan George
Journal of Aerospace Information Systems pp 1-12; https://doi.org/10.2514/1.i010990

Abstract:
Aerospace mission autonomy can improve mission capabilities, reduce the burden on ground operators, and prevent mission failure when encountering unexpected situations. However, autonomy comes at the cost of additional complexity for design, development, and testing. This research presents an autonomous mission framework to bridge the gap between existing aerospace mission systems and emerging autonomous paradigms. Traditional aerospace missions without custom autonomy solutions generally follow rigid schedules, and tasks are executed during specified time windows. For these types of scenarios, the only task constraint is the current time. Our proposed framework, called the Schedule Manager, expands on this concept by adding additional constraint checks such as conflict, priority, and resource constraints. Through the design and upload of task schedules, constraint tables, and contingency schedules, the benefits of autonomy can be achieved without deviating significantly from the standard, thereby simplifying the adoption, integration, and safety analysis process. This framework was integrated and tested with NASA’s core Flight System and 42 spacecraft simulator, and run on development boards comparable to flight hardware.
, Shady K. Saied, Mohamed A. Elshafey
Journal of Aerospace Information Systems pp 1-9; https://doi.org/10.2514/1.i010903

Abstract:
Synthetic aperture radar (SAR) enables imaging of topographic surfaces day and night in different atmospheric conditions. SAR imaging systems record both intensity and phase information of the backscattered signals. Acquired intensity information is often exposed to speckle noise, and gathered phase information is usually corrupted by thermal and other types of noise. Thus, these types of noises have negative effects on interpretation of SAR images. Digital elevation model (DEM) can be generated by interferometric SAR using two SAR images, of the same area, with slightly different look angles. The generated DEM is affected by the corruption of both intensity and phase information. In this paper, a proposed framework of convolutional neural network (CNN) and modified Wiener filter (MWF) is suggested in DEM generation process. The main purpose of the proposed framework is minimizing not only speckle noise of input SAR images but also phase noise of the interferogram. Thus, an enhanced DEM can be generated. Extensive experiments are carried out and different DEMs are generated from original SAR and from both despeckled SAR images and filtered interferogram. Results and comparative analyses show significant improvements in both quality and vertical accuracy of the DEM generated by the proposed hybrid (CNN-MWF) framework.
Colton Gingrass, , Michael P. Atkinson
Journal of Aerospace Information Systems pp 1-12; https://doi.org/10.2514/1.i010923

Abstract:
The recent widespread implementation of Automatic Dependent Surveillance–Broadcasting (ADS-B) systems on aircraft allows for improved monitoring and air traffic control management. As part of this monitoring, it is important to be able to detect unusual flight trajectories due to weather events, detection avoidance, aircraft malfunction, or other activities that may signal anomalous behavior. Given the large volume of ADS-B data available from aircraft around the world, the ability to automatically determine the shape of the trajectory and identify anomalous behavior is important to reduce the need for human identification and labeling. A neural network model is developed for multicategory classification of the shape of the trajectory using features derived from a large ADS-B data set such as bearing and curvature. The results suggest promise in differentiating common trajectory shapes using key factors, with the accuracy of the classifier being comparable to human accuracy.
Changkoo Kang, Haseeb Chaudhry, , Kevin Kochersberger
Journal of Aerospace Information Systems, Volume 18, pp 645-658; https://doi.org/10.2514/1.i010909

Abstract:
Two image-based sensing methods are merged to mimic human vision in support of airborne detect-and-avoid and counter–unmanned aircraft systems applications. In the proposed sensing system architecture, a peripheral vision camera (with a fisheye lens) provides a large field of view, whereas a central vision camera (with a perspective lens) provides high-resolution imagery of a specific target. Beyond the complementary ability of the two cameras and supporting algorithms to enable passive detection and classification, the pair forms a heterogeneous stereo vision system that can support range resolution. The paper describes development and testing of a novel peripheral–central vision system to detect, localize, and classify an airborne threat. The system was used to generate a dataset for various types of mock threats in order to experimentally validate parametric analysis of the threat localization error. A system performance analysis based on Monte Carlo simulations is also described, providing further insight concerning the effect of system parameters on threat localization accuracy.
, P. Paul Gesting, James A. Pogemiller, Denise L. Brown
Journal of Aerospace Information Systems, Volume 18, pp 605-615; https://doi.org/10.2514/1.i010889

Abstract:
This paper presents a method for modeling and simulation of Global Navigation Satellite System (GNSS) constellations by the use of Chebyshev polynomials fit to publicly available precision data. The Global Positioning System (GPS) is used to demonstrate the method, but the method applies to all GNSS with precise ephemeris data including Galileo and GLONASS. The method facilitates highly accurate satellite orbit and clock modeling, including relativistic effects, with mean fit differences of the order 10−4 m in position, 10−8 m/s in velocity, and 10−4 microseconds in clock offset. The method also improves simulation run time by using a fast technique for evaluating the Chebyshev polynomials. Runtime comparisons demonstrate the polynomial method reduces runtime by up to 72% of more traditional methods. The speed of the method makes it well suited for practical, accurate Monte Carlo simulation techniques. The method can be used in simulations for the verification and validation of GNSS receivers embedded in autonomous aerospace navigation systems, for uncertainty analysis, and for other purposes. Details for validating the precision data are given. Pseudocode algorithms are provided for fitting and evaluating Chebyshev polynomials. Also provided are high-level concepts of one approach for integrating the method into larger, multisystem aerospace simulations, including the effects of signal propagation delay and Earth blockage.
, Eric Johnson, Eric Feron, Brian German
Journal of Aerospace Information Systems, Volume 18, pp 616-631; https://doi.org/10.2514/1.i010924

Abstract:
Unmanned aerial systems (UASs) are continuing to proliferate. Quantitative risk assessment for UAS operations, both a priori and during the operation, are necessary for governing authorities and insurance companies to understand the risks and properly approve operations and assign insurance premiums, respectively. In this paper, the problem of UAS risk analysis and decision making is treated through a novel application of Dempster–Shafer (DS) networks using auto-updating transition matrices. This method was motivated by the results of the 2018 UAS Safety Symposium held at the Georgia Institute of Technology, which was conducted as part of the research detailed in this paper. The paper describes training a DS network based on simulated operation data, testing the capabilities of the trained network to make real-time decisions on a small UAS against a baseline system in a representative mission, and exploring how this system would extend to a more inclusive UAS ecosystem. Conclusions are drawn with respect to the research performed, and additional research directions are proposed.
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