Multi-Sensor Data Integration for Autonomous Sense and Avoid

Operations in the Next Generation Air Transportation System (NextGen) are planned to have a reduced dependency on primary surveillance radar and use Automatic Dependent Surveillance - Broadcast (ADS-B) to provide aircraft state information to Air Traffic Control (ATC) and onboard collision detection and avoidance systems. With the ongoing integration of Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS), ADS-B is expected to become the main surveillance input to UAS collision avoidance. Traffic Alert and Collision Avoidance System (TCAS) is unable to meet requirements in terms of adaptability to future operations with reduced separation margins, addition of new user groups (e.g., UAS), and support of additional surveillance inputs. Airborne Collision Avoidance System (ACAS X) is being designed to meet these requirements and to increase surveillance, alerting, and resolution performance. The RTCA has released Minimum Operational Performance Standards (MOPS) to provide system and operational performance characteristics that ADS-B must provide. This paper analyzes recorded ADS-B data from test flights that were performed in the Continental U.S. (CONUS) and verifies measured performance with specified operational characteristics in terms of coverage, accuracy, and integrity.

In-Trail Procedures (ITP) is an application of Automatic Dependent Surveillance-Broadcast (ADS-B) that allows aircraft to change flight levels in areas where current non-radar separation standards would prevent desirable altitude changes. The ITP avionics receive ADS-B Out position reports from aircraft at intermediate flight levels that could be blocking the desired climb or descent maneuver. The most recent standard for ADS-B broadcast is RTCA/DO-260B [1]. This is also known as Version 2. Equipage is mandated by 2020 in the US for the same aircraft that carry transponders. Equipage is mandated in Europe by 2015 for forward fit aircraft and by 2017 for retrofit aircraft. Standards have been developed for the ADS-B data accuracy requirements for ITP [2, 3]. The standards are straightforward to apply to aircraft equipped with transponders certified to DO-260B [1]. The standards are less straightforward to apply for aircraft equipped with transponders certified to the earlier standards of ADS-B, which are known as Version 0 or Version 1 [4, 5]. This paper shows that crosschecking ADS-B data with Traffic Alert and Collision Avoidance System (TCAS) data is an effective mitigation technique for uncertain ADS-B horizontal position accuracy and integrity for legacy Version 0 and Version 1 ADS-B transponders.

The University of North Dakota has been actively developing enabling airborne sense and avoid (SAA) technologies under a series of projects funded by the Department of Defense, and has selected the Automatic Dependent Surveillance – Broadcast (ADS-B) transceiver as one of its central components for cooperative aircraft mid-air collision avoidance. The system integrates a 4D (i.e., latitude, longitude, altitude, and time) SAA algorithm dependent on UAS dynamic performance factors. This paper presents the results of implementation and flight testing of a miniaturized collision avoidance system using ADS-B derived information. The flight test results that took place onboard a small UAS in the summer of 2010 demonstrate that a complex airborne SAA system utilizing an ADS-B transceiver can be integrated into a small UAS vehicle to improve safety and eventually allow integration of UAS vehicles into the U.S. National Airspace System (NAS).

<div class="htmlview paragraph">This paper provides a summary of the data and findings from a ten-month evaluation of the Traffic Alert and Collision Avoidance System (TCAS) II Industry Prototype installed on a Piedmont Airlines' Boeing 727 aircraft. The data presented represents quantitative and qualitative information on the performance of TCAS II in the National airspace System (KAS) and identifies the impacts of TCAS on Air Traffic Control and the flight crew. The information provided concentrates on the characteristics of the TCAS encounters and the operational use of the system.</div>

Cooperative and non-cooperative Sense-and-Avoid (SAA) capabilities are key enablers for Unmanned Aircraft Vehicle (UAV) to safely and routinely access all classes of airspace. In this paper state-of-the-art cooperative and non-cooperative SAA sensor/system technologies for small-to-medium size UAV are identified and the associated multi-sensor data fusion techniques are introduced. A reference SAA system architecture is presented based on Boolean Decision Logics (BDL) for selecting and sorting non-cooperative and cooperative sensors/systems including both passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B). After elaborating the SAA system processes, the key mathematical models associated with both non-cooperative and cooperative SAA functions are presented. The analytical models adopted to compute the overall uncertainty volume in the airspace surrounding an intruder are described. Based on these mathematical models, the SAA Unified Method (SUM) for cooperative and non-cooperative SAA is presented. In this unified approach, navigation and tracking errors affecting the measurements are considered and translated to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UAV) and various intruder platforms. Results corroborate the validity of the proposed approach and demonstrate the impact of SUM towards providing a cohesive logical framework for the development of an airworthy SAA capability, which provides a pathway for manned/unmanned aircraft coexistence in all classes of airspace.

: A computer analysis was conducted to investigate the effect of an omnidirectional version of the Traffic Alert and Collision Avoidance System (TCAS) on the performance of: (1)the Air Traffic Control Radar Beacon System (ATCRBS), and (2) the planned Discrete Address Beacon System (DABS) in selected air traffic environments. The performance of ATCRBS and DABS was examined both with and without the TCAS in operation. Additional simulations were conducted to quantify the effect of TCAS when employing its interference-limiting function.

This paper studies the design method of intermediate frequency digital processing for Traffic Alert and Collision Avoidance System (TCAS) receiver and mode S transponder. The system adopts full Field-Programmable Gate Array (FPGA) design to realize 64MHz high-speed digital signal processing. Since the digital processing of TCAS receiver and mode S transponder in the middle frequency segment and the consistency of baseband processing, the design concept of integrated avionics system and software radio technology are applied in the design of TCAS receiver and mode S transponder, so as to change the traditional joint system architecture and realize the shared platform resources.

This paper discusses the requirements for surveillance and collision avoidance equipment in the National Airspace System (NAS), the current avionics equipage by the U.S. aircraft fleet, and the GA avionics market in general. The operational and functional requirements for a general aviation traffic alert system are presented accompanied by concept design, development and schedule information. The paper also discusses the issue of achieving compatibility with the existing Traffic Alert and Collision Avoidance System (TCAS) community. This paper contains only the understanding and views of the authors and is not intended to reflect the official position or view of the U.S. Government or Federal Aviation Administration (FAA). >

This paper reports on the research done under a National Aeronautics and Space Administration (NASA) Research Announcement task, “Assessment of System Safety Risks for NextGen Concepts and Technologies.” We describe the methods and tools used in the task and results from first-order analyses of two NextGen ground-based automation concepts. The first-order results demonstrate the ability of the assessment method developed in previous NASA research to address NextGen concepts and to link research results rigorously to systemwide risk. The method explicitly addresses response times and failures, and the results, while first-order, specifically identify the time criticality of tactical conflict detection and resolution. I. Introduction n this research, we extended and applied to automated NextGen concepts the separation assurance safety risk assessment method we developed for the NASA Research Announcement task, “Conduct Safety Analysis of the Separation Assurance Function in Today’s National Airspace System (NAS)” (Ref. 1). As in our previous work, we addressed the task objective in two ways. First, we established functional and quantitative frameworks to allow integration of the results of all forms of safety and risk research into a single risk assessment. Second, we identified sources of data and developed and tested modeling tools to demonstrate the feasibility of our analysis approach. We conducted first-order risk assessments of two NextGen concepts: a Ground-Automation Controlled (GAC) concept based on the Advanced Airspace Concept (AAC) proposed by Erzberger in Ref. 2, and a near-term GroundAutomation Assisted (GAA) concept based on the concept proposed by McNally et al. in Ref. 3. Both concepts are based on managing traffic with ground-based automation using data link communications to control aircraft on timebased four-dimensional trajectories (4DTs). This paper describes analysis of the fully operational concepts plus two abnormal conditions. The first abnormal condition is a loss of intent data condition, which could be caused by a weather or emergency disruption leading to open 4DTs or by a hardware failure leading to loss of transmitted intent data; the second is a single aircraft inoperative transponder condition, which could be caused by hardware failure or by being turned off. Both concepts include a ground-based strategic element, a ground-based tactical element, an airborne Traffic Alert and Collision Avoidance System (TCAS) element, and an airborne see-and-avoid element. The AAC concept includes a monitoring human controller element, while the human-based GAA concept includes an active human controller in the strategic and tactical elements. The paper is organized as follows: 1) Section II presents our aviation safety risk analysis method and introduces the two NextGen concepts assessed. 2) Section III discusses the conflict modeling tool and the conflict probability data used for the risk assessments.

A unified approach to cooperative and non-cooperative Sense-and-Avoid (SAA) is presented that addresses the technical and regulatory challenges of Unmanned Aircraft Systems (UAS) integration into non-segregated airspace. In this paper, state-of-the-art sensor/system technologies for cooperative and non-cooperative SAA are reviewed and a reference system architecture is presented. Automated selection of sensors/systems including passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B) system is performed based on Boolean Decision Logics (BDL) to support trusted autonomous operations during all flight phases. The BDL adoption allows for a dynamic reconfiguration of the SAA architecture, based on the current error estimates of navigation and tracking sensors/systems. The significance of this approach is discussed in the Communication, Navigation and Surveillance/Air Traffic Management and Avionics (CNS+A) context, with a focus on avionics and ATM certification requirements. Additionally, the mathematical models employed in the SAA Unified Method (SUM) to compute the overall uncertainty volume in the airspace surrounding an intruder/obstacle are described. In the presented methodology, navigation and tracking errors affecting the host UAS platform and intruder sensor measurements are translated to unified range and bearing uncertainty descriptors. Simulation case studies are presented to evaluate the performance of the unified approach on a representative UAS host platform and a number of intruder platforms. The results confirm the validity of the proposed unified methodology providing a pathway for certification of SAA systems that typically employ a suite of non-cooperative sensors and/or cooperative systems.

This paper proposes a new traffic alert and collision avoidance system (TCAS) for general aviation (GA) that uses automatic dependent surveillance-broadcast (ADS-B). The proposed GA-TCAS alerts the pilot of upcoming potential conflicts, which are becoming increasingly common as the number of GA movements increases. Conflict detection and avoidance uses the ADS-B and GA maneuvers. The algorithm selects the angle according to the intruder’s heading and position in order to reduce the nuisance alerts. The detection algorithm has three stages: proximate advisory, traffic advisory, and resolution advisory. The relationship between the turn angle and the horizontal miss distance (HMD) are calculated to determine a resolution when an intruder enters the separation bubble. Using the encounter models for visual flight rule (VFR) to evaluate the performance of this TCAS algorithm, the resolution should follow the VFR and prioritize in different situations. In flight simulations, the proposed GA-TCAS is tested using real ultralight flight data. The system performance is demonstrated using flight trajectories from conflict detection and resolution.

Safety-critical software must be thoroughly verified before being exploited in commercial applications. In particular, any TCAS (Traffic Alert and Collision Avoidance System) implementation must be verified against safety properties extracted from the anti-collision theory that regulates the controlled airspace. This verification step is currently realized with manual code reviews and testing. In our work, we explore the capabilities of Constraint Programming for automated software verification and testing. We built a dedicated constraint solving procedure that combines constraint propagation with Linear Programming to solve conditional disjunctive constraint systems over bounded integers extracted from computer programs and safety properties. An experience we made on verifying a publicly available TCAS component implementation against a set of safety-critical properties showed that this approach is viable and efficient.

As a result of the FAA Modernization and Reform Act of 2012, Unmanned Aerial Systems (UAS) are expected to be integrated into the National Airspace System (NAS) by 2015. Several human factors challenges need to be addressed before UAS can safely and routinely fly in the NAS with manned aircraft. Perhaps the most significant challenge is for the UAS to be non-disruptive to the air traffic management system. Another human factors challenge is how to provide UAS pilots with intuitive traffic information in order to support situation awareness (SA) of their airspace environment as well as a see-and-avoid capability comparable to manned aircraft so that a UAS pilot could safely maneuver the aircraft to maintain separation and collision avoidance if necessary. A simulation experiment was conducted to examine baseline compliance of UAS operations in the current airspace system. Researchers also examined the effects of introducing a Cockpit Situation Display (CSD) into a UAS Ground Control Station (GCS) on UAS pilot performance, workload and situation awareness while flying in a positively controlled sector. Pilots were tasked with conducting a highway patrol police mission with a Medium Altitude Long Endurance (MALE) UAS in L.A. Center airspace with two mission objectives: 1) to reroute the UAS when issued new instructions from their commander, and 2) to communicate with Air Traffic Control (ATC) to negotiate flight plan changes and respond to vectoring and altitude change instructions. Objective aircraft separation data, workload ratings, SA data, and subjective ratings regarding UAS operations in the NAS were collected. Results indicate that UAS pilots were able to comply appropriately with ATC instructions. In addition, the introduction of the CSD improved pilot SA and reduced workload associated with UAS and ATC interactions.

We demonstrate a novel method to reliably generate collision avoidance advisories, in piloted simulations, by the widely used traffic alert and collision avoidance system (TCAS). The TCAS advisory issued to a pilot is highly sensitive to the trajectory of an intruder aircraft relative to the ownship flown by the pilot. In realistic piloted simulations, a prescripted intruder trajectory will not reliably result in the relative dynamics that lead to a desired TCAS advisory. Further, the complexity of the TCAS logic requires a novel method for mapping trajectories to the range of possible advisories. We propose to use a rapidly exploring random tree algorithm in large-scale fast-time simulations to establish the mapping between the space of relative trajectories and TCAS advisories. These trajectories are then created in piloted simulations through guidance algorithms. Results demonstrate the ease of use and robustness of this method, and its potential for pilot training and for research and development.

Software testing is essential in the software development process. Modified Condition / Decision Coverage (MC/DC) is a test case derivation technique that enhances the stability and reliability of software by effectively verifying complex conditions and decision structures. We propose the MCC Test Cases (MTC) generator in this study. This generator generates Multiple Condition Coverage (MCC) test cases using binary numbers to confirm the maximum coverage value of MC/DC testing. The proposed MTC generator utilizes some conditions from the Traffic Alert and Collision Avoidance System (TCAS)-II specification. It converts them into a Comma-Separated Values (CSV) file and then validates the coverage results through the VectorCAST program. So, MC/DC testing was performed using the MCC test case to confirm the maximum coverage value when performing MC/DC tests for each condition of the TCAS-II specification. This research is helpful for the verification of MC/DC test cases by confirming the maximum coverage value when performing MC/DC tests. Moreover, having more test cases increases the likelihood of discovering defects. Therefore, it can improve the efficiency of software test coverage verification, as well as the quality and stability of software.