Aircraft collision prediction based on binomial distribution

Introduction/purpose: Based on the binomial distribution of the probability density function, a new probabilistic model for aircraft position predicting is presented in this paper. Methods: The proposed algorithm is composed of three different blocks: Data Association, Tracking/Hybrid State Estimation and Calculation of Probability of Conflict. The information about aircraft current positions and flight plans is used to derive an algorithm for detecting possible conflicts between aircraft. The situations where aircraft may come closer than a certain distance to one another are predicted with high probability. The position estimate and indeterminacy refer to target association when two tracks fall in a validation region by using the Probabilistic Data Association Filter. Results: An efficient collision detection algorithm is designed and tested for a lot of multiple target tracking. Conclusion: The simulation results of aircraft conflict prevention in two trajectory scenarios verify the efficiency of the proposed algorithm.