Abstract
For the tracking of multiple maneuvering targets under radar observations, the Cardinality-Balanced Multi-Bernoulli based Sequential Monte-Carlo Filter (SMC-CBMeMBer) tracking algorithm gets its shortcomings that the estimation of number is inaccurate and the state estimation accuracy is degraded. This paper presents an improved tracking algorithm based on SMC-CBMeMBer smoothing filter. In the prediction process, the algorithm uses Multi-objectIve Particle Swarm Optimization (MOPSO), combined with the measured values at the current moment, to move the particles to the location where the posterior probability density distribution takes a larger value; Besides the smooth recursive method is used to smooth the filter value with multi-target measurement data, and the estimation accuracy of the algorithm is improved on the basis of sacrificing certain operation efficiency. The simulation results show that compared with the traditional filter and smoothing methods, the proposed algorithm performs better in terms of the accuracy of the estimation of the number of maneuvering targets and the accuracy of the target state estimation.
This work was supported in part by the National Science Foundation of China (U1633122, 61531020, 61501487), National Defense Science Foundation (2102024), the China Postdoctoral Science Foundation (2017M620862) and by the Special Funds of Taishan Scholars Construction Engineering, Young Elite Scientist Sponsorship Program of CAST.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Pei, J., Huang, Y., Dong, Y., Chen, X. (2018). MOPSO Optimized Radar CBMeMBer Forward-Backward Smoothing Filter. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_60
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