2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)

Conference Information
Name: 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)
Location: Sacramento, United States
Date: 2016-9-25 - 2016-9-29

Latest articles from this conference

Mary Ellen Miller, Eduardo Colon Madera, Ovid Sekhar
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-7; https://doi.org/10.1109/dasc.2016.7777972

Abstract:
Although the Aeronautical Information Exchange Model (AIXM) and ICAO Weather Information Exchange Model (IWXXM) standards are quite mature, the implementation of those standards is open to interpretation. Differences between International Air Navigation Service Providers' (ANSPs) implementations have been observed during the development phase of the Mini Global (MG) Phase II project. These differing interpretations in the implementation of AIXM and IWXXM standards could jeopardize interoperability. This paper focuses on the observed differences and the solutions developed for the MG II demonstration for AIXM and IWXXM interoperability between the United States, Asia, Caribbean, Europe, and Middle Eastern regions.
Steven D. Young, Taumi Daniels, Emory Evans, Evan Dill, Maarten Uijt de Haag, Tim Etherington
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-11; https://doi.org/10.1109/dasc.2016.7777954

Abstract:
Airplane state awareness (ASA) is a pilot performance attribute derived from the more general attribute known as situation awareness. Airplane state alludes primarily to attitude and energy state, but also infers other state variables, such as the state of automated or autonomous systems, that can affect attitude or energy state. Recognizing that loss of ASA has been a contributing factor to recent accidents, an industry-wide team has recommended several Safety Enhancements (SEs) to resolve or mitigate the problem. Two of these SEs call for research and development of new technology that can predict energy and/or auto-flight system states, and intuitively notify or alert flight crews to future unsafe or otherwise undesired states. In addition, it is desired that future air vehicles will be able to operate with a high degree of awareness of their own well-being. This form of ASA requires onboard predictive capabilities that can inform decision-making functions of critical markers trending to unsafe states. This paper describes a high-fidelity flight simulation study designed to address the two industry-recommended SEs for current aircraft, as well as this desired self-awareness capability for future aircraft. Eleven commercial airline crews participated in the testing, completing more than 220 flights. Flight scenarios were utilized that span a broad set of conditions including several that emulated recent accidents. An extensive data set was collected that includes both qualitative data from the pilots, and quantitative data from a unique set of instrumentation devices. The latter includes a head-/eye-tracking system and a physiological measurement system. State-of-the-art flight deck systems and indicators were evaluated, as were a set of new technologies. These included an enhancement to the bank angle indicator; predictive algorithms and indications of where the auto-flight system will take the aircraft and when automation mode changes will occur or where energy-related problems may occur; and synoptic (i.e., graphical) depictions of the effects of loss of flight critical data, combined with streamlined electronic checklists. Topics covered by this paper include the research program context, test objectives, descriptions of the technologies under test, platform and operational environment setup, a summary of findings, and future work.
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-5; https://doi.org/10.1109/dasc.2016.7778041

Abstract:
The sustained air traffic growth worldwide has resulted in the need for upgrading the existing global air traffic management (ATM) system. Consequently, a new standard called L-band Digital Aeronautical Communication System (LDACS) is being developed for the air-ground communications component of the next generation ATM systems. Amongst the options being considered for LDACS, the LDACS type 1 (LDACS1) is the most superior and mature candidate and is likely to be the final choice for deployment. The presence of previously deployed and operational legacy systems in the L-band has led to stringent spectral mask specifications for LDACS1, to ensure there is no interference caused to the former. In this paper, we propose the design of a digital filter bank which can satisfy the LDACS1 spectral mask specifications while also achieving low complexity of implementation. Design of the proposed filter bank is based on the improved coefficient decimation method. With the help of a design example, we show that when compared with the discrete Fourier transform based filter bank, the proposed filter bank achieves 71.49% reduction in multiplication complexity.
Ken O'Neill, G. Richard Newell, Sathish Kumar Odiga
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-7; https://doi.org/10.1109/dasc.2016.7777979

Abstract:
Today's commercial aircraft are e-Enabled, with networked connectivity to ground stations for real-time health monitoring, location tracking, and maintenance. Once the systems are connected to the network, security is a major challenge. Connected systems are vulnerable to cybersecurity threats. To counteract the cybersecurity threats, a system of layered security measures must be applied from the system power-up. A hardware root-of-trust is a secure foundation for implementing secure systems. It must provide the reliability essential for DO-254 compliant systems, and industry standard cryptographic algorithms and protocols. This is possible through the use of flash-based secure FPGAs which provide advanced security capabilities, SRAM-PUF based key management, and anti-tamper countermeasures. The advanced security and reliability capabilities of the flash-based secure FPGAs meet the requirements for hardware root-of-trust in avionics applications. The e-Enabled aircraft must have the security measures to authenticate software from power-up, securely communicate with ground stations, and perform secure remote software updates. This paper presents a security framework and the methodologies necessary to implement these security measures using flash-based secure FPGAs as hardware root-of-trust.
Tom Guillaumet, Aayush Sharma, Eric Feron, Madhava Krishna, Ranjani Narayan, Philippe Baufreton, Francois Neumann, Emmanuel Grolleau
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-6; https://doi.org/10.1109/dasc.2016.7777978

Abstract:
With the onset of multi- and many-core chips, the single-core market is closing down. Those chips constitute a new challenge for aerospace and safety-critical industries in general. Little is known about the certification of software running on these systems. There is therefore a strong need for developing software architectures based on multi-core architectures, yet compliant with safety-criticality constraints. This paper presents a reconfigurable multi-core architecture and the safety-criticality constraints for airborne systems. The last section uses the current certification guidance to explain how the architecture can satisfy these constraints even with dynamic features activated.
Pengfei Phil Duan, Maarten Uijt De Haag, Tim Etherington, Laura Smith-Velazquez
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-12; https://doi.org/10.1109/dasc.2016.7778085

Abstract:
The lack of aircraft state awareness has been one of the leading causal and contributing factors in aviation accidents. Many of these accidents were due to flight crew's inability to understand the automation modes and properly monitor the aircraft energy and attitude state. The capability of providing flight crew with improved airplane state awareness (ASA) is essential in ensuring aviation safety. This paper focusses on predictive alerting methods to achieve improved ASA and describes the methods used to predict (a) stall and overspeed conditions, (b) high-and-fast conditions, (c) low-and-slow conditions, (d) unstable approach conditions, and (e) automation mode transitions. The proposed method estimates and subsequently predicts the aircraft state based on (i) aircraft state related information output by the onboard avionics, (ii) the configuration of the aircraft, (iii) appropriate aircraft dynamics models of both the active modes and the modes to which can be transitioned via simple pilot actions, and (iv) accurate models of the uncertainty of the dynamics and sensors. Onboard avionics inputs include measurements from onboard navigation systems such as global navigation satellites systems (GNSS), inertial navigation systems, and air data. This paper provides a detailed description of the prediction algorithms, the predictive alerting display concepts, and some test results based on flight data collected during a recent NASA flight simulator study in which eleven commercial airline crews (22 pilots) completing more than 230 flights.
, Magnus Nylin, Billy Josefsson
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-9; https://doi.org/10.1109/dasc.2016.7777949

Abstract:
As in many other areas, air traffic control has faced challenges in the reality of digitalization and automation. Despite the introduction of new technology, runway incursions are a persisting problem at airports. A runway incursion that develops into an incident or accident is the No 1 risk for the Air Navigation Service Providers. World-wide ATM tower work relies on variations of a generic design and tool set with a high degree of similarities, but with varying degrees of digitalization. Our study investigates the roles of ATM systems in the development of runway incursions, and on the potential to address them through further digitalization. This case study is based on the digitalized tower environment of Arlanda airport, and has a special focus on the electronic flight strips. Six episodes from a human-in-the loop simulation of Arlanda tower are described in detail, based on audio/video and eye gaze recordings. Four of the episodes contained irregularities of which two were runway incursions. Results showed that the e-strip system conceptually still very much plays the same role as the old paper strips it replaced, not taking full advantage of the possibilities of a digitalized system. It also showed that the systems in the tower environment are often not sharing information and that the human operator is very much left alone to gather and interpret the information from the different systems. The conclusion is that there are some significant design challenges ahead for to create the ATM system for the future with maintained or increased safety and performance with improved human-automation collaboration.
Jeffrey Homola, Thomas Prevot, Joey Mercer, Nancy Bienert, Conrad Gabriel
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-7; https://doi.org/10.1109/dasc.2016.7778078

Abstract:
NASA has engaged in collaborative research with the FAA and many other stakeholders in government, industry, and academia to explore the concepts and requirements necessary to enable the safe and scalable application of small unmanned aircraft systems (UAS) in low-altitude airspace. In this effort, the UAS Traffic Management (UTM) project has developed a multi-faceted simulation component that supports near-term live flight testing in addition to further term concept exploration. This paper provides an overview of the simulation capabilities currently available as part of the UTM project and the laboratory environment in which they are applied.
K. Niki Maleki, Kaveh Ashenayi, Loyd R Hook, Justin G Fuller, Nathan Hutchins
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-5; https://doi.org/10.1109/dasc.2016.7778103

Abstract:
Despite the tremendous attention Unmanned Aerial Vehicles (UAVs) have received in recent years for applications in transportation, surveillance, agriculture, and search and rescue, as well as their possible enormous economic impact, UAVs are still banned from fully autonomous commercial flights. One of the main reasons for this is the safety of the flight. Traditionally, pilots control the aircraft when complex situations emerge that even advanced autopilots are not able to manage. Artificial Intelligence based methods and Adaptive Controllers have proven themselves to be efficient in scenarios with uncertainties; however, they also introduce another concern: nondeterminism. This research endeavors to find a solution on how such algorithms can be utilized with higher reliability. Our method is based on using an adaptive model to verify the performance of a control parameter — proposed by a nondeterministic adaptive controller or AI-based optimizer — before it is deployed on the physical platform. Furthermore, a backup mechanism is engaged to recover the drone in case of failure. A Neural Network is employed to model the aircraft, and a Genetic Algorithm is utilized to optimize the PID controller of a quadcopter. The initial experimental results from test flights indicate the feasibility of this method.
Evan T. Dill, Steven D. Young, Kelly J. Hayhurst
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) pp 1-10; https://doi.org/10.1109/dasc.2016.7778009

Abstract:
As demands increase to use unmanned aircraft systems (UAS) for a broad spectrum of commercial applications, regulatory authorities are examining how to safely integrate them without loss of safety or major disruption to existing airspace operations. This work addresses the development of the Safeguard system as an assured safety net technology for UAS. The Safeguard system monitors and enforces conformance to a set of rules defined prior to flight (e.g., geospatial stay-out or stay-in regions, speed limits, altitude limits). Safeguard operates independently of the UAS autopilot and is strategically designed in a way that can be realized by a small set of verifiable functions to simplify compliance with regulatory standards for commercial aircraft. A framework is described that decouples the system from any other devices on the UAS as well as introduces complementary positioning source(s) for applications that require integrity and availability beyond what the Global Positioning System (GPS) can provide. Additionally, the high level logic embedded within the software is presented, as well as the steps being taken toward verification and validation (V&V) of proper functionality. Next, an initial prototype implementation of the described system is disclosed. Lastly, future work including development, testing, and system V&V is summarized.
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