Journal of Aerospace Information Systems

Journal Information
ISSN / EISSN : 1940-3151 / 2327-3097
Total articles ≅ 534
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Paveen Juntama, Daniel Delahaye, Supatcha Chaimatanan, Sameer Alam
Journal of Aerospace Information Systems pp 1-16; https://doi.org/10.2514/1.i011048

Abstract:
Airspace capacity has become a critical resource for air transportation. Complexity in traffic patterns is a structural problem, whereby airspace capacity is sometimes saturated before the number of aircraft has reached the capacity threshold. This paper addresses a strategic planning problem with an efficient optimization approach that minimizes traffic complexity based on linear dynamical systems in order to improve the traffic structure. Traffic structuring techniques comprise departure time adjustment, en route trajectory deviation, and flight-level allocation. The resolution approach relies on the hyperheuristic framework based on reinforcement learning to improve the searching strategy during the optimization process. The proposed methodology is implemented and tested with a full day of traffic in the French airspace. Numerical results show that the proposed approach can reduce air traffic complexity by 92.8%. The performance of the proposed algorithm is then compared with two different algorithms, including the random search and the standard simulated annealing. The proposed algorithm provides better results in terms of air traffic complexity and the number of modified trajectories. Further analysis of the proposed model was conducted by considering time uncertainties. This approach can be an innovative solution for capacity management in the future air traffic management system.
Hongchuan Wei, Keith A. LeGrand, Andre A. Paradise,
Journal of Aerospace Information Systems pp 1-12; https://doi.org/10.2514/1.i010934

Abstract:
Minimizing the amount of communication required by a sensor network is crucial to minimizing both energy and time consumption, as well to operating covertly and robustly in communication-contested environments. This paper presents a novel intermittent communication control approach applicable to sensor networks deployed to sense and model spatio-temporal processes by nonparametric models such as Gaussian processes (GPs). The approach relies on a novel and efficient approximation of the GP average generalization error (AGE), as well as on novel GP sensor control and regression methods presented in this paper. This novel AGE approximation allows each sensor to characterize the nominal prediction performance of the learned GP model in the absence of communications. As a result, individual sensors can update the GP hyperparameters based solely on local measurements and decide to communicate only if and when their estimate of the nominal prediction performance falls below an acceptable threshold.
Georges Ghazi,
Journal of Aerospace Information Systems pp 1-20; https://doi.org/10.2514/1.i011050

Abstract:
In this paper, a mathematical model for estimating the performance and flight trajectories in cruise is identified from data available in flight manuals. The first part of this paper focuses on the design of a fuel flow and emissions model. Starting from the equations of motion of an aircraft in cruise, a simplified model representing the fuel flow in a corrected form was developed. A practical algorithm was next developed to identify the aircraft model parameters and to determine the mathematical structure that reflects its fuel flow. This process was done using performance data available in the aircraft flight crew operating manual. The emissions model was also developed based on data available in the International Civil Aviation Organization’s engine emissions databank. The second part of the paper deals with the development of algorithms for predicting the trajectories and calculating the optimal speeds (i.e., maximum range, long range, and economy) of the aircraft in cruise. Practical techniques for storing and retrieving information without using optimization algorithms have been considered. The methodology was applied on both a Cessna Citation X business jet and Bombardier CRJ-700 regional jet aircraft. The comparison results showed a very good agreement for the fuel consumption and optimal speed.
Mark A. Foster
Journal of Aerospace Information Systems pp 1-7; https://doi.org/10.2514/1.i010985

Abstract:
The environment in low Earth orbit (LEO) is congested with orbital debris threatening the safety of all states, actors, and enterprises who wish to use space. Removing orbital debris is unquestionably one of the top space challenges facing space actors of this generation, one that demands cross-disciplinary solutions to solve this complex systems engineering problem. Based on a review of past and recent approaches to active debris removal, many of the currently proposed technologies and systems focus on removing defunct satellites and other large derelict objects in LEO that are actively cataloged and tracked. While removing the larger space objects is a necessary goal, space sustainability experts agree that the orbital debris ranging from 4 mm to 9 cm poses the greatest risk to operational spacecraft and presents unique challenges for its removal. This paper offers a novel technical approach for the development and near-term deployment of an orbital debris removal system based on existing technology that would be capable of reducing lethal untracked orbital debris in LEO.
Xiwen Yang, , Defu Lin, Yadong Wang
Journal of Aerospace Information Systems, Volume 19, pp 355-365; https://doi.org/10.2514/1.i011037

Abstract:
This paper proposes a distributed positioning algorithm for a swarm of unmanned aerial vehicles (UAVs) to track multiple moving targets with bearing-only measurements. An approximate performance metric is first derived that can be used in position determination based on the properties of multisensor joint probabilistic data association (JPDA) filter. A fully distributed position planning algorithm using incremental optimization strategy is then proposed for tracking multiple moving targets. Simulation examples with comparison results validate the effectiveness of the proposed algorithm.
Minkyu Lee, Geemoon Noh, Jihoon Park,
Journal of Aerospace Information Systems, Volume 19, pp 330-343; https://doi.org/10.2514/1.i011016

Abstract:
In this paper, a path planning algorithm is proposed for dynamic obstacle avoidance by improving a rapidly exploring random tree (RRT) algorithm, a sampling-based path-planning algorithm. Although guaranteeing a globally optimal path is impossible when using RRT algorithm, it is advantageous because it can generate the path quickly. Considering this advantage of the RRT algorithm, the directed RRT (DRRT) algorithm was developed, which reduces the generation time and supplements optimality by improving node generation and tree expansion process. It is also added the path smoothing method for the flight. The anytime DRRT algorithm is based on the DRRT algorithm and repeats path generation within the time limit considering the direction of movement of the obstacle and selects the shortest path for the flight. Air collision avoidance between unmanned aerial vehicles (UAVs) considering the intruders as a dynamic obstacle was simulated using the Gazebo simulator. Based on the simulation, the performance of the proposed anytime DRRT algorithm was verified with varying the host UAV and intruder conditions, and its applicability to UAVs was affirmed.
, Wendy A. Okolo
Journal of Aerospace Information Systems, Volume 19, pp 382-393; https://doi.org/10.2514/1.i011018

Abstract:
Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems, operations, and infrastructure, to the system. To this end, historical data on airline scheduling and operations recovery are used to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, this paper provides a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.
Siyu Su, , Longbiao Li, Chong Peng, Haitao Zhang, Tingting Zhang
Journal of Aerospace Information Systems pp 1-15; https://doi.org/10.2514/1.i011011

Abstract:
The engine bleed air system (BAS) is one of the important systems for civil aircraft, and its operating state directly affects the operational safety of the aircraft. The effective risk warning of the BAS is critical to improving aircraft safety and operators’ profits; thus, a multivariate state estimation technique with the dynamic process memory matrix is proposed to warn the failure risk of the bleed air system. First, to obtain the optimal estimation value of the observation vector, the memory matrix is formed by searching for the first M vectors that are similar to each input observation vector from the healthy data pool that can cover the common working space. Then, the similarity function is defined to quantitatively measure the deviation between the observed vector and the estimated vector, and the amount of risk information contained in each variable is quantified by the analytic hierarchy process. Finally, the dynamic threshold different from the traditional engineering experience threshold is designed, based on the idea of interval estimation. The developed approach is validated on an Airbus A320-series aircraft with quick access recorder data for one year. The results show that the proposed strategy can provide an effective risk warning for the abnormal state of the BAS before a failure occurs.
Junyi Geng, Puneet Singla,
Journal of Aerospace Information Systems, Volume 19, pp 366-381; https://doi.org/10.2514/1.i011067

Abstract:
This paper studies a load-distribution-based trajectory planning and control strategy for a hierarchically controlled multilift system. It proposes a method that simultaneously plans payload trajectory and cable forces while satisfying path and force constraints and minimizing the difference in cable forces. A direct collocation method is used to solve the formulated planning problem. Then, a neighboring feedback law is designed to equalize the cable tension load distribution during flight. Here, the system dynamics are linearized about the nominal path. An linear-quadratic regulator (LQR) controller is then designed for the system to track the planned trajectory. Simulations of payload transport showed that even with the effect of disturbances (i.e., wind gusts), cable tensions are more evenly distributed with the proposed approach. Finally, indoor flight tests were performed to validate the proposed approach. Results showed that the system has reduced energy consumption compared with the case without planning based on load distribution. The rotorcraft achieved less average total power and near-equal energy consumption.
Yingxiao Kong, Sankaran Mahadevan
Journal of Aerospace Information Systems, Volume 19, pp 344-354; https://doi.org/10.2514/1.i011033

Abstract:
Aircraft landing is one of the riskiest phases of flight with multiple possible adversities, such as sudden gust, misalignment, ground vehicle incursion, hard landing, and runway overrun. A long landing distance increases the risk of landing overrun, which appears frequently in landing accidents. In this paper, we develop a landing distance prediction approach using DASHlink data. Time dependence in the time series flight data is captured by a long short-term memory neural network model. A multistep rolling prediction strategy is developed to predict the landing distance, which captures the temporal variation of flight parameters better compared to a single-step prediction. The methodology is accompanied by several preprocessing steps, such as upsampling/downsampling, data smoothing, removal of outliers, and standardization. Several different modeling options within the overall methodology are investigated to identify the best performing model. The proposed methodology is illustrated with landing data at the Detroit Metropolitan Wayne County Airport (KDTW), and the performances of several modeling options are compared with each other as well as with several other well-established modeling methods.
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