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Moving target parameter estimation for MIMO radar based on the improved particle filter

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Abstract

In this paper, a novel independent partition Rao-Blackwellized particle filter (IPRBPF) is proposed to estimate the moving target parameters in MIMO radar. Firstly, noticing that the likelihood function is a nonlinear function of the nonlinear position parameters, and that the target motion equation is a linear function of linear parameters such as velocity, acceleration and etc. The nonlinear particle filter is proposed to estimate the nonlinear position parameters and the linear Kalman filter is proposed to estimate the linear parameters. Then a new MIMO radar parameter estimation algorithm based on Rao-Blackwellized particle filter is obtained. Furtherly, considering that the computational complexity will increase dramatically with the targets’ state dimension in the case of multiple targets, the independent partition sampling is put forward to improve the performance of our algorithm, then the IPRBPF algorithm is obtained. Compared with the existing methods, the proposed algorithm can achieve the lower computational complexity and the higher accuracy of parameter estimation. Simulation results demonstrate the advantages of the proposed algorithm.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61101172, 61371184 and 61301262) and the Fundamental Research Funds for the Central Universities of Ministry of Education of China (Grant No. ZYGX2014J021).

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Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Jinfeng Hu, Hanwen Chen, Yang Li, Huiyong Li & Julan Xie

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  1. Jinfeng Hu
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  2. Hanwen Chen
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  3. Yang Li
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  4. Huiyong Li
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  5. Julan Xie
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Correspondence to Jinfeng Hu.

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Hu, J., Chen, H., Li, Y. et al. Moving target parameter estimation for MIMO radar based on the improved particle filter. Multidim Syst Sign Process 29, 1–17 (2018). https://doi.org/10.1007/s11045-016-0447-7

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  • DOI: https://doi.org/10.1007/s11045-016-0447-7

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