| Citation: | CHEN Weigao, JIA Xin, ZHU Weigang, et al. Radar state transfer estimation method based on HMM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10): 2171-2180. doi: 10.13700/j.bh.1001-5965.2016.0836(in Chinese)
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Aimed at the problem of traditional recognition models that parameter rule description is not complete, a hierarchical model suitable for multi-function radar (MFR) is proposed in this paper. This model reflects the operating mechanism of MFR system through three levels of task, state and parameter. Then according to parameter features, a variety of functions are used to describe the change rule of parameters, and signal joint changes and statistical information can be reflected. This model has better recognition effect compared with statistic and pulse sample diagram model. On the basis of the hierarchical model, to solve the problem of poor robustness and low accuracy of MFR state transfer estimation method, double chain hidden Markov model (HMM) was built by introducing target motion state information. D-S (Dempster-Shafer) evidence theory was used to optimize estimated results, and a radar state transfer estimation method based on HMM was proposed. The experimental results show that the proposed algorithm has more excellent robustness and higher estimation accuracy rate than that before improvement.
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