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Open AccessArticle

Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking

by Qi Deng 1,2, Gang Chen 1,2,* and Huaxiang Lu 1,2,3,4
1
High Speed Circuits and Neural Networks Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
2
University of Chinese Academy of Sciences, Beijing 100089, China
3
Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing 100089, China
4
CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4278; https://doi.org/10.3390/app9204278
Received: 7 August 2019 / Revised: 24 September 2019 / Accepted: 9 October 2019 / Published: 12 October 2019
High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the effectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios. View Full-Text
Keywords: target tracking; unscented particle filter; partitioned sampling; adaptive sample size target tracking; unscented particle filter; partitioned sampling; adaptive sample size
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Deng, Q.; Chen, G.; Lu, H. Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking. Appl. Sci. 2019, 9, 4278.

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