General aviation collision avoidance-challenges of full implementation

<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>

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.

: 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.

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.

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.

To operate Unmanned Aerial Systems (UAS) freely in the National Airspace System (NAS), an on-board sense-and-avoid (SAA) capability equivalent to or better than manned aircraft will be required. Under U.S. Air Force Research Laboratory (AFRL) sponsorship, Northrop Grumman Corporation (NGC) has been developing a scalable autonomous SAA system using a comprehensive sensor suite comprising Traffic Alert and Collision Avoidance System (TCAS) and Automatic Dependent Surveillance Broadcast (ADS-B) for detecting cooperative intruders as well as radar and electro-optical (EO) sensors for detecting noncooperative intruders. This paper focuses on the sensor data integration (SDI) portion of the autonomous SAA system in the areas of design objective, architectural and algorithmic approach, and flight test results.

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.

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.

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 advantage of a head-up auditory display for situational awareness was evaluated in an experiment designed to measure and compare the acquisition time for capturing visual targets under two conditions: standard head-down Traffic Alert and Collision Avoidance System display and three-dimensional (3-D) audio Traffic Alert and Collision Avoidance System presentation. (The technology used for 3-D audio presentation allows a stereo headphone user to potentially localize a sound at any externalized position in 3-D auditory space). Ten commercial airline crews were tested under full-mission simulation conditions at the NASA-Ames Crew-Vehicle Systems Research Facility Advanced Concepts Flight Simulator. Scenario software generated targets corresponding to aircraft that activated a 3-D aural advisory (the head-up auditory condition) or a standard, visual-audio TCAS advisory (map display with monaural audio alert). Results showed a significant difference in target acquisition time between the two conditions, favoring the 3-D audio Traffic Alert and Collision Avoidance System condition by 500 ms.

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.

Machine vision represents a particularly attractive solution for sensing and detecting potential collision‐course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and Traffic Alert and Collision Avoidance System). This paper describes the development and evaluation of a real‐time, vision‐based collision‐detection system suitable for fixed‐wing aerial robotics. Using two fixed‐wing unmanned aerial vehicles (UAVs) to recreate various collision‐course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400 to about 900 m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advance warning of between 8 and 10 s ahead of impact, which approaches the 12.5‐s response time recommended for human pilots. We overcame the challenge of achieving real‐time computational speeds by exploiting the parallel processing architectures of graphics processing units (GPUs) found on commercial‐off‐the‐shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real‐time processing of 1,024 × 768 pixel image frames at a rate of approximately 30 Hz. Flight trials using manned Cessna aircraft in which all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms. © 2010 Wiley Periodicals, Inc.

This paper describes the specification-based testing, analysis tools, and associated processes that were used to independently validate, verify, and ultimately provide for certifying, safety-critical software developed for the Traffic Alert and Collision Avoidance System (TCAS II) program. These tools and processes comprise an effective and Independent Validation and Verification (IV&V) activity applied to the Collision Avoidance Subsystem (CAS) software development process. A requirements specification language called the Requirements State Machine Language (RSML), originally developed by the University of California, Irvine (UCI), was employed for the specification of CAS. The end result is the next generation of TCAS II collision avoidance logic, referred to as Version 7, that is of a higher quality than its predecessors, meets the certification requirements of DO-178B Level B, and can be shown to satisfy the new operational requirements it was developed to address.

Air Traffic Management helps to ensure all National Airspace System (NAS) operators have the opportunity to safely accomplish their operational goals while maintaining NAS efficiency. Advancements in technology and a robust global demand for access to space are leading to new space flight participants and increasing numbers of space launch and reentry operations, adding complexity to NAS management coordination and decision-making processes. Diversity of launch and reentry profiles and locations, coupled with increased activity [1, 2], will impact airspace management planning by requiring increased coordination among the Federal Aviation Administration (FAA) and other federal agencies, civilian facilities, and commercial operators. Current processes will not scale as space activity in the NAS increases. The MITRE Corporation is working with the FAA to improve launch and reentry operations efficiency and repeatability. The approach includes analyzing current processes and identifying how more standardized communication mechanisms, integrated information flows, and consistent analysis capabilities can be applied. This paper describes a concept for sharing information and integrating it as part of a standardized, repeatable process that can help the FAA meet future space activity needs in the NAS. A key feature is to integrate information related to NAS effects assessment into the process to facilitate rapid analysis and feedback.

A number of Federal Aviation Administration (FAA) automation systems and developmental programs interoperate to support the day-to-day operations of the National Airspace System (NAS). The integration of New Entrants (NEs), such as Remotely Piloted Aircraft (RPA) 1 and Space Vehicle (SV) operations, into the NAS will require modification to the current state of automation. Examining the relevant operational, technical, and procedural implications of integrated NE operations, in addition to exploring the potential impacts of accelerated RPA usage on NAS dynamics, are critically important efforts necessary to understanding operational impacts to NAS systems and users. Our paper presents the potential impacts of RPA integration on two of these systems: En Route Automation Modernization (ERAM) and Traffic Flow Management System (TFMS). A significant component of the impact on NAS systems comes from flight plan and related data processing since all SVs and many RPAs will fly non-standard flight patterns (e.g., RPA loitering and grid search). The processes and details of the non-traditional flight routes, as well different RPA performance specifications, need to be considered as missions are overlaid on the national jet route structure. Of particular concern and interest are the changes in air traffic control (ATC) and air traffic management (ATM) systems and sub-systems needed to address air traffic controller workload and workload complexity concerns associated with NE activities. One potential accommodation approach, presented in this paper, is the use of Dynamic Airspace Configuration both for RPA routine operations and contingency events. Additionally, we present an RPA 1 In this paper, UAS and RPA are used interchangeably without intending any difference per se. It is noted that the U.S. DoD and ICAO use RPA while NASA and the FAA use UAS. demand forecast tool developed under a National Aeronautics and Space Administration (NASA) project. This tool will assist the FAA in meeting its objectives for accommodation of RPA by providing accurate demand forecast information.