雷达鸟类目标微多普勒贝叶斯增强算法

doi:10.12305/j.issn.1001-506X.2024.02.15

Abstract

Abstract:

Aiming at the problem that it is difficult to detect bird target using the traditional constant false-alarm rate (CFAR) method, a Bayesian enhancement algorithm of the micro-Doppler feature of bird target based on the time-frequency (TF) domain is proposed. Firstly, based on the flapping model of bird target, the corresponding radar echo signal and micro-Doppler model are established orderly. Secondly, the short-time Fourier transform (STFT) is considered to analyze the echo signal in TF domain. In view of the problem that resolution is influenced by windowing process of STFT and STFT is sensitive to clutter, a generalized Gaussian distribution (GGD) is introduced to model the prior in an adaptive way, and the micro-Doppler feature in TF domain is enhanced in a Bayesian inference manner. Considering the difficulty of calculating the non-smooth posterior distribution related to the micro-Doppler feature of the target, the proximal unadjusted Langevin algorithm (P-ULA) is proposed to solve it efficiently. Experimental results of simulated and raw data show that the proposed can not only effectively suppress the background noise, but also preserve the continuity of micro-Doppler feature to some extent.

Key words: radar detection for bird target, micro-Doppler feature, short-time Fourier transform, generalized Gaussian distribution (GGD), Bayesian learning

CLC Number:

TN95

Lei YANG, Weitian SUN, Xinyao MAO, Yabo XIA, Jinghe SANG. Bayesian enhancement algorithm for micro-Doppler feature of radar bird target[J]. Systems Engineering and Electronics, 2024, 46(2): 505-516.

Figures/Tables 13

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Bayesian enhancement algorithm for micro-Doppler feature of radar bird target

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