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21 July 2023 An adaptive interactive multiple-model algorithm with time-varying Markov transition matrix for maneuvering target tracking
Fang Wang, Cheng Cheng, Wujun Zou, Shubiao Guo
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Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127172I (2023) https://doi.org/10.1117/12.2684773
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China
Abstract
Maneuvering target tracking technology is widely used in military scenarios. To solve the hysteresis problem of maneuvering target motion model transition in interactive multiple-model technology, an adaptive interactive multiple-model algorithm with time-varying Markov transition matrix is proposed. The proposed method constructs time-varying Markov transition matrix based on the differential between the motion model probabilities at adjacent time steps. Meanwhile, it is combined with particle filter to get a better estimation accuracy for maneuvering target tracking. The proposed algorithm is verified in experimental simulation compared with traditional interactive multiple-model algorithm, and it is shown that average tracking error decreases by 18.5% and the probability of matched motion model of maneuvering target can increase rapidly when the target maneuvers.
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Fang Wang, Cheng Cheng, Wujun Zou, and Shubiao Guo "An adaptive interactive multiple-model algorithm with time-varying Markov transition matrix for maneuvering target tracking", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127172I (21 July 2023); https://doi.org/10.1117/12.2684773
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KEYWORDS
Motion models
Detection and tracking algorithms
Particle filters
Computer simulations
Nonlinear filtering
Complex systems
Signal filtering
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