基于非监督贝叶斯学习雷达性能指标动态评估

doi:10.3969/j.issn.1001-506X.2021.01.10

Abstract

Abstract:

In view of conventional evaluation method of radar performance index is relatively tedious and lack of theoretical bounds, which requires enough repeated experiments. This results in low efficiency and high cost of implementation. A fully dynamic evaluation algorithm for the radar performance index is proposed based on the unsupervised Bayesian statistical learning method. Combined with typical observed data model, the posterior probability model of radar performance index is established under a certain prior assumption of radar target. Considering the possible non-conjugated characteristic of prior and observation data, a hierarchical Bayesian model for the prior probability model is introduced, which guarantees the solvability of the intended posterior probability function for the radar performance index. In addition, to ensure closed form solutions of the posterior probability function, the method of variational Bayesian expectation maximization (VB-EM) is utilized, so that the posterior probability function of the performance index and the hyper-parameters can be calculated through Gauss-Seidel iterative strategy. Finlly, the analytical estimates of corresponding performance indexs, and their confidential interval and confidential degree can be acquired by using the results of posterior probability function, which can realize the analytical indication of the dynamic change of index. Compared with the traditional Monte Carlo evaluations, the proposed method can obtain quantitative and analytical index evaluation results with single piece of experimental data, which can greatly reduce the evaluation cost and improve the evaluation efficiency. At the same time, it can give a quantitative indication of the dynamic changes of the index. The simulation data is applied to verify the accuracy of radar positioning and height measurement as well as target detection probability index. Compared with the traditional methods, the gain of the evaluation can be improved in a great extent.

Key words: unsupervised learning, Bayesian learning, dynamic evaluation, radar performance index

CLC Number:

TN957

Lei YANG, Xinyao MAO, Xiaowei YANG, Hai ZHANG, Fei YANG, Lin SUN. Dynamic evaluation of radar performance index based on unsupervised Bayesian learning[J]. Systems Engineering and Electronics, 2021, 43(1): 74-82.

Figures/Tables 7

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Dynamic evaluation of radar performance index based on unsupervised Bayesian learning

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