Research on Air Route Conflict Detection for General Aviation based on ADS-B
28 May 2021-
TL;DR: In this article, a three-level collision avoidance system based on ADS-B technology applied in general aviation is proposed, which includes RA collision avoidance, TA collision avoidance and protected area (PA) collision avoidance.
Abstract: Based on the research of TCAS and other collision avoidance systems applied in transportation aviation, a three-level collision avoidance system based on ADS-B technology applied in general aviation is proposed. The system includes RA collision avoidance, TA collision avoidance and protected area (PA) collision avoidance. The PA area collision avoidance can realize collision avoidance in advance, which makes full use of the advantages of ADS-B's wide working range, It is also in line with the characteristics of free navigation and difficult collision avoidance of general aviation. Route conflict detection is the premise of PA collision avoidance. An effective route conflict detection algorithm is proposed. The algorithm first decomposes the route conflict problem into three-dimensional coordinate axis conflict problems and detects them separately. When each coordinate axis has conflict, it combines decision detection. During the detection, the conflict problem of three-dimensional coordinate axis is mapped into conflict time period to determine the intersection, It solves the problem of conflict judgment. Based on the algorithm, the simulation experiment of route conflict detection is carried out for 100 times with 1000 intruding aircraft within 100 km each time. The result shows that only about 5 ‰ of the intruding aircraft have conflict. The experimental results are in line with the actual situation, which also proves the effectiveness of the algorithm. The principle of the proposed algorithm is clear, the amount of calculation is small, simple and practical.
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TL;DR: In this paper , a multi-scale grid modeling and coding mapping method of airspace information represented by ADS-B is put forth, and tests on the validity of the 4D airspace-temporal grid were conducted across key areas based on the development of an effective data organization method for ADS-b, or an effective algorithm for extracting relevant spatiotemporal data.
Abstract: With the exponential increase in the volume of automatic dependent surveillance-broadcast (ADS-B), and other types of air traffic control (ATC) data containing spatiotemporal attributes, it remains uncertain how to respond to immediate ATC data access within a target area. Accordingly, an original multi-level disaggregated framework for airspace, and its corresponding information management is proposed. Further, a multi-scale grid modeling and coding mapping method of airspace information represented by ADS-B is put forth. Finally, tests on the validity of the 4D airspace-temporal grid we named as the GeoSOT-AS framework were conducted across key areas based on the development of an effective data organization method for ADS-B, or an effective algorithm for extracting relevant spatiotemporal data. Experimentally, it was demonstrated that GeoSOT-AS conforms to the existing Chinese specification of civil aeronautical charting and is advantageous for its low deformation and high practicality; furthermore, the airspace grid identification code modeling was less costly, and improved performance by >80% when used for ADS-B data extraction. GeoSOT-AS can thus provide effective reference and practical information for existing airspace data management methods represented by ADS-B and can subsequently be extended to other forms of airspace management scenarios.
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TL;DR: A method to estimate the probability that a conflict will occur, given a pair of predicted trajectories and their levels of uncertainty is presented.
Abstract: The safety and efficiency of free flight will benefit from automated conflict prediction and resolution advisories. Conflict prediction is based on trajectory prediction and is less certain the farther in advance the prediction, however. An estimate is therefore needed of the probability that a conflict will occur, given a pair of predicted trajectories and their levels of uncertainty. This paper presents a method to estimate that conflict probability. The trajectory prediction errors are modeled as normally distributed, and the two error covariances for an aircraft pair are combined into a single, equivalent covariance of the relative position. A coordinate transformation is then used to derive an analytical solution. Numerical examples and a Monte Carlo validation are presented. (Author)
384 citations
TL;DR: An algorithm for decentralized conflict detection and resolution that generalizes potential fields methods for path planning to a probabilistic dynamic environment is proposed and validated using Monte Carlo simulations.
Abstract: Conflict detection and resolution schemes operating at the mid-range and short-range level of the air traffic management process are discussed. Probabilistic models for predicting the aircraft position in the near-term and mid-term future are developed. Based on the mid-term prediction model, the maximum instantaneous probability of conflict is proposed as a criticality measure for two aircraft encounters. Randomized algorithms are introduced to efficiently estimate this measure of criticality and provide quantitative bounds on the level of approximation introduced. For short-term detection, approximate closed-form analytical expressions for the probability of conflict are obtained, using the short-term prediction model. Based on these expressions, an algorithm for decentralized conflict detection and resolution that generalizes potential fields methods for path planning to a probabilistic dynamic environment is proposed. The algorithms are validated using Monte Carlo simulations.
299 citations
TL;DR: A framework for conflict resolution that allows one to take into account such levels of uncertainty using a stochastic simulator is presented and it is shown how the cost criterion can be selected to ensure an upper bound on the probability of conflict for the optimal maneuver.
Abstract: The safety of flights, and, in particular, separation assurance, is one of the main tasks of air traffic control (ATC). Conflict resolution refers to the process used by ATCs to prevent loss of separation. Conflict resolution involves issuing instructions to aircraft to avoid loss of safe separation between them and, at the same time, direct them to their destinations. Conflict resolution requires decision making in the face of the considerable levels of uncertainty inherent in the motion of aircraft. In this paper, a framework for conflict resolution that allows one to take into account such levels of uncertainty using a stochastic simulator is presented. The conflict resolution task is posed as the problem of optimizing an expected value criterion. It is then shown how the cost criterion can be selected to ensure an upper bound on the probability of conflict for the optimal maneuver. Optimization of the expected value resolution criterion is carried out through an iterative procedure based on Markov chain Monte Carlo. Simulation examples inspired by current ATC practice in terminal maneuvering areas and approach sectors illustrate the proposed conflict resolution strategy
150 citations
TL;DR: In this article, a method known as conflict probability estimation (CPE) is presented to estimate the probability of conflict for pairs of aircraft with uncertain predicted trajectories, which is a generalization of a generalisation of a...
Abstract: A method known as conflict probability estimation (CPE) is presented to estimate the probability of conflict for pairs of aircraft with uncertain predicted trajectories. It is a generalization of a...
73 citations
TL;DR: This paper investigates the introduction of a sample-based representation of state uncertainty to an existing algorithm called Real-Time Belief Space Search (RTBSS), which leverages branch-and-bound pruning to make searching the belief space for the optimal action more efficient.
Abstract: The aircraft collision avoidance problem can be formulated using a decision-theoretic planning framework where the optimal behavior requires balancing the competing objectives of avoiding collision and adhering to a flight plan. Due to noise in the sensor measurements and the stochasticity of intruder state trajectories, a natural representation of the problem is as a partially-observable Markov decision process (POMDP), where the underlying state of the system is Markovian and the observations depend probabilistically on the state. Many algorithms for finding approximate solutions to POMDPs exist in the literature, but they typically require discretization of the state and observation spaces. This paper investigates the introduction of a sample-based representation of state uncertainty to an existing algorithm called Real-Time Belief Space Search (RTBSS), which leverages branch-and-bound pruning to make searching the belief space for the optimal action more efficient. The resulting algorithm, called Monte Carlo Real-Time Belief Space Search (MC-RTBSS), is demonstrated on encounter scenarios in simulation using a beacon-based surveillance system and a probabilistic intruder model derived from recorded radar data.
55 citations