2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)最新文献

{"title":"Advanced Functions for Lowering Nuisance Alerts in a DAA System: Implementation and Performance Evaluation in Real-Time Human-in-the-Loop Testing","authors":"G. Corraro, F. Corraro, N. Genito, Gianluigi Di Capua, E. Filippone, Ciniglio Umberto","doi":"10.1109/DASC43569.2019.9081698","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081698","url":null,"abstract":"Over the past several decades, Remotely Piloted Aircraft Systems (RPAS) have experienced a large spreading into a number of applications, however, the current operational limitations imposed by the Aviation Authorities concerning safety, make their integration in the controlled airspace a great challenge. To this aim, the detect and avoid (DAA) systems play a fundamental role giving to the RPAS the capability to detect conflicting traffic or other hazards and take appropriate actions in order to guarantee the airspace safety. In this framework, the present work describes the DAA module, named ADAPT, that the Italian Aerospace Research Centre (CIRA) is developing. Such system merges several aspects of DAA state-of-the-art introducing some improvements to decrease the nuisance alerts and increase the situational awareness of the remote pilot through the customization of the graphical appearance of the human machine interface. The effectiveness of the developed DAA system has been demonstrated through the execution of a real-time human-in-the-loop test campaign of which some remarkable results are reported.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115750882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multiple Hypothesis Tracking Approach to Collision Detection for Unmanned Aerial Vehicles","authors":"F. d'Apolito, C. Sulzbachner, Felix Bruckmueller","doi":"10.1109/DASC43569.2019.9081705","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081705","url":null,"abstract":"It is obvious that future unmanned aerial vehicle (UAV) applications will have a high degree of automation, up to autonomy, and will be integrated into civil airspace to go beyond line of sight. To ensure the safe operation of these systems, new technologies are required. Currently, the regulatory and technological frameworks are insufficient. For instance, there are no standards for sensors required to detect obstacles and avoid possible collisions. It is evident that data from several sensors need to be combined to obtain a robust environmental map in order to cope with the high standards of aviation. This paper aims to introduce a Multiple Hypothesis Tracking (MHT) approach to multi-sensor data fusion for future collision avoidance systems. The data from several sensors are merged to obtain new and more accurate measurement data to capture the environment more robustly. The sensors consist of several optical and thermal sensors, radar and transponder systems. This combined measurement data is processed by an avoidance algorithm to calculate avoidance maneuvers. So far, simulations and ground based tests have shown that the implemented MHT approach provides qualitatively better results than conventional probabilistic data fusion approaches. In a next step, test flights will be performed to evaluate the proposed MHT data fusion approach in an airborne environment.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115828977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Modules and Advanced Displays to Support Pilot Airplane System State Awareness","authors":"S. Whitlow, Michael B. Dillard","doi":"10.1109/DASC43569.2019.9081762","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081762","url":null,"abstract":"The loss of pilot airplane state awareness (ASA) has been implicated as a factor in a number of aviation accidents identified by the Commercial Aviation Safety Team (CAST). These accidents were investigated to identify precursors to the loss of ASA and develop technologies to address the loss of ASA. Based on a gap analysis, two technologies were prototyped and assessed in a formative pilot-in-the-loop evaluation. The technologies address: 1) data source anomaly detection in realtime, and 2) intelligent monitoring aids to provide nominal and predictive awareness of situations to be monitored and a mission timeline to visualize events of interest. The evaluation results indicated favorable impressions of both technologies for mitigating the loss of ASA in terms of workload, acceptability, complexity, and usability.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117352958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and Ways Forward for Avionics Platforms and their Development in 2019","authors":"B. Annighoefer, M. Halle, A. Schweiger, Marina Reich, C. Watkins, S. Vanderleest, S. Harwarth, Patrick Deiber","doi":"10.1109/DASC43569.2019.9081794","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081794","url":null,"abstract":"Today's air vehicles depend on digital technology. It accounts for more than 30% of their development costs. The number of functions, the lines of code, the degree of autonomy, and the number of vehicles rise. This is why there is a need for cutting-edge technology and development methods. There is a gap between academia's methods and industrial applications due to multi-disciplinary challenges. We summarize the state-of-the-art in avionics, namely avionics platforms, requirements engineering, model-based development, automated verification, emerging technologies, and emerging demands. Experts review the most demanding challenges, research gaps, and promising solutions. They provide recommendations for the enhancement of the cooperation between industry and academia and suggest necessary research topics. This article is an introduction for those who are new to avionics. It is an up-to-date summary, for insiders looking for most promising solutions to their current problems; and it is a guide for those advancing avionics research.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123467912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Air-to-Air Wideband Channel Measurements between Small Unmanned Aerial Vehicles with Optical Fibers","authors":"Dennis Becker, L. Schalk","doi":"10.1109/DASC43569.2019.9081691","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081691","url":null,"abstract":"In order to ensure safe and efficient operation and to prevent collisions, unmanned aerial vehicles (UAVs) need to communicate with each other with high reliability. To design respective communication systems, accurate air-to-air (A2A) channel models are needed, especially for urban environments, where the channel characteristics are hard to predict due to rich multipath propagation, diffractions and non line of sight (LOS) conditions. For these models, channel measurements in different scenarios are inevitable to model the real-world communication channel. However, small sized UAVs are very limited in carrying payload and in power supply, making it difficult or often impossible to use high performing channel-sounding hardware equipment. As a result, less resource demanding hardware with lower performance in the sense of clock synchronization, time resolution or dynamic range is usually applied leading to a limited propagation channel characterization. In this work, we describe a measurement setup that allows using arbitrary channel sounder hardware by exploiting analog optical links in order to enable A2A wideband channel measurements between small sized UAVs. We extend the operation of our MEDAV RUSKDLR channel sounder by guiding a 100 MHz bandwidth radio frequency (RF) signal at 5.2 GHz through two 600 m long optical fibers attached on two hexacopters after being converted with high bandwidth converters and show the feasibility of this setup with first flight trials in an urban scenario.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115184555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban Area Unmanned Aerial Systems Sensor Capabilities for Ensuring Ground Hazards Safety","authors":"Xavier Bouyssounouse, C. Ippolito","doi":"10.1109/DASC43569.2019.9081759","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081759","url":null,"abstract":"Unmanned aerial systems (UASs) in urban areas can pose significant safety risks to dynamic ground objects (DGOs) such as people, pets, and bikes; especially for off-nominal emergency traverses and landings. This paper will examine a framework for evaluating the UAS safety benefits which can be achieved by classifying DGO hazards, modeling their behavior, and assigning collision costs. DGOs are assumed to be any ground objects which are either moving or capable of moving. Safety benefits will be assessed by analyzing metrics computed from UAS and DGO trajectories which take into account intent and uncertainties. This paper will establish the theoretical relationships mapping these trajectories and DGO classifications to safety levels. Sensor capabilities will be mapped to DGO trajectory uncertainties, so that safety can be directly estimated from the sensor specifications for a given UAS trajectory.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114382610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measured Impact of ADS-B In Applications on General Aviation and Air Taxi Accident Rates","authors":"D. Howell, J. King","doi":"10.1109/DASC43569.2019.9081643","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081643","url":null,"abstract":"While General Aviation (GA) and Air Taxi accident rates have been declining over time, the remaining accidents still result in a substantial loss of life and property. One way the FAA is addressing GA and Air Taxi safety is through the Surveillance and Broadcast Services (SBS) program. SBS uses satellite-enabled technology known as Automatic Dependent Surveillance-Broadcast (ADS-B) to provide applications that use traffic and weather information received in the cockpit on the ADS-B frequency (ADS-B In). This document describes current SBS-enabled cockpit applications and explores their impact on accident rates in two separate analyses: the first examines the impact of ADS-B In on GA (Federal Aviation Regulations [FAR] Part 91) and Air Taxi (FAR Part 135) users in the Contiguous United Sates (CONUS); the second considers Air Taxi operators in Alaska. The results indicate a reduction in relevant accident rates from 40 to 60 percent for ADS-B In equipped aircraft.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114412735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detect and Avoid (DAA) Alerting Performance Comparison: CPDS vs. ACAS-Xu","authors":"Timothy Grebe, Fabrice Kunzi","doi":"10.1109/DASC43569.2019.9081673","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081673","url":null,"abstract":"Detect and Avoid (DAA) systems are integral to an Unmanned Aircraft System's (UAS) ability to Remain Well Clear (RWC) of other aircraft; they are an enabling technology for UAS to integrate into the National Airspace System (NAS). “Minimum Operational Performance Standards (MOPS) for Detect and Avoid (DAA) Systems”1(RTCA DO-365) specifies criteria for determining when an alerting algorithm must, may, and must not issue an alert in an encounter with an intruding aircraft. Various organizations have developed prototype alerting algorithms for DAA, including National Aeronautics and Space Administration (NASA)'s Detect and AvoID Alerting Logic for Unmanned Systems (DAIDALUS), General Atomics Aeronautical Systems, Inc. (GA-ASI)'s Conflict Prediction and Display System (CPDS), and the Airborne Collision Avoidance System-Xu (ACAS-Xu) algorithms. This paper evaluates CPDS and ACAS Xu using the performance metrics explained in RTCA DO-365, and the observed strengths and shortcomings are identified and summarized. The data comes from NASA flight tests (FTs) 2 and 4; which occurred in 2017 and 2016, respectively. The former tested a Phase 2 DAA implementation using ACAS Xu run 3, while the latter tested a Phase 1 implementation using CPDS to perform the RWC function, and TCAS II v7.1 to fulfill the Collision Avoidance (CA) function. In summary, while both algorithms issued the majority of alerts within the MOPS requirements, CPDS tended to out-perform ACAS Xu. CPDS had fewer early alerts and late alerts, indicating that ACAS Xu may need adjustments to tune for MOPS compliance.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114477111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyber Awareness Trends in Avionics","authors":"E. Blasch, R. Sabatini, A. Roy, K. Kramer, G. Andrew, G. Schmidt, C. Insaurralde, G. Fasano","doi":"10.1109/DASC43569.2019.9081646","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081646","url":null,"abstract":"Recent examples driving an increased need for cyber awareness include unmanned aerial vehicles (DAVs) in the airspace, development of the Automatic dependent surveillance—broadcast (ADS-B), and the risk of cyber intrusion. The incident of a civilian UAV disrupting a major airport, is one example of many incidents raising questions on the future of airspace security. While a civilian hobbyist might be ignorant of the impending harm, the situation could pose a threat to the air operations. Using this incident and others like it, ideas on the future trends in cyber awareness applied to avionics are considered. Three general ideas are incorporated: (1) mandates for new rules for airspace restrictions (e.g., geofence), (2) advanced display analytics to support the airport staff and pilots with visual displays, such as collision avoidance, and (3) electronics to support the mitigation of electronic cyber effects posed by the obtrusive aircraft. With a variety of opinions posed, the summary is intended to foster a discussion on research, industry, and commercial directions for the avionics community to consider with respect to cyber awareness.","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114592911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fail-Safe, Fail-Secure Experiments for Small UAS and UAM Traffic in Urban Airspace","authors":"Sam Siewert, K. Sampigethaya, Jonathan M. Buchholz, Steve Rizor","doi":"10.1109/DASC43569.2019.9081710","DOIUrl":"https://doi.org/10.1109/DASC43569.2019.9081710","url":null,"abstract":"Millions of small Unmanned Aerial Systems (sUAS), or drones weighing less than 55 pounds, will fly in urban airspaces within a decade. This is in addition to goals for increased Urban Air Mobility (UAM): larger transportation UAS and piloted air vehicles, including air taxi service and innovative aviation designs to revolutionize transportation. A significant problem is the lack of current management systems for this extremely large volume of air traffic, composed of heterogeneous drone shapes, sizes, and onboard equipment capabilities, flying adjacent to larger UAM vehicles in spaces previously unoccupied by General Aviation (GA). Robust and resilient detection, identification, localization, and tracking of sUAS sharing UAM airspace is complicated by urban factors such as non-transmitting, non-cooperative drones. Exacerbating these challenges, current technological limitations and vulnerabilities in Global Positioning System (GPS), Automatic Dependent Surveillance-Broadcast (ADS-B), and Inertial Navigation Systems (INS) can pose safety and security threats. To streamline a vision of safe and secure sUAS-UAM shared airspace, we propose Drone Net, a UAS Traffic Management (UTM) network of multi-modal ground and flight instruments. Drone Net is an architecture fusing a network of passive visual and acoustic sensor nodes with active Radio Detection and Ranging (radar) and Light Detection and Ranging (lidar) sensing methods for UTM, designed for integration with existing Air Traffic Control (ATC) and future UAM systems. The purpose of the Drone Net system is to evaluate use of Electro-Optical/Infrared (EO/IR) and acoustic arrays networked within an urban UAS operating region such as uncontrolled Class-G and waiver-granted Class-D airspace. The Drone Net approach combines detection, tracking, and localization estimation from radar, ADS-B, and EO/IR such that the design is robust to sensor errors, sample loss, and spoofing or other types of attacks. The system experimental design allows for emulation of corrupted ADS-B, GPS, and INS data on our flight platform including capability to communicate with ground radar and EO/IR to recover from flight instrument major and minor malfunctions such that a Drone Net cooperative UAS can be engineered to be fail-safe and fail-secure. We hypothesize that the Drone Net network of rooftop sensors, shown below, provides this conceptualized fail-safe, fail-secure property based upon the combination of self-localization with backup and confirming data from the ground sensor network. Further, with ground-system sensor fusion as well as UTM flight plan and registration information fusion, the overall system can manage small UAS that are both compliant and non-compliant alongside GA and UAM more safely than a single mode like ADS-B alone. To test our hypothesis, we envision two experiments this year. First, a test to confirm that we can emulate ADS-B, GPS, and INS sensor data corruption, leading to recovery using backup fl","PeriodicalId":129864,"journal":{"name":"2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116873134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}