基于因子分析的样本缺失信号重构算法

Anjun Chen, Baoshuai Wang, Jiacheng Wu
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引用次数: 0

摘要

雷达回波中的微多普勒调制特征能够反映目标的几何结构和运动特征,广泛应用于目标参数提取和模式识别。针对短停留时间下微多普勒分辨率低的问题,提出了一种基于因子分析(FA)模型的样本缺失信号重建算法。首先利用遗传算法对未知完整信号进行描述,然后建立样本缺失观测信号与未知完整信号之间的数学模型。然后利用贝叶斯理论将其转化为全概率模型。采用变量贝叶斯期望最大化法(VBEM)对模型进行求解,从而得到完整信号的重构。同时,针对FA因子数量的确定问题,在模型中引入自动相关确定(ARD)先验,实现因子数量的自动确定。基于实测数据的实验结果表明,该方法比传统的压缩感知(CS)方法具有更好的重构性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconstruction algorithm of sample missing signal based on factor analysis
The micro-Doppler modulation feature in radar echo can reflect the geometric structure and motion characteristics of targets, and is widely used in target parameter extraction and pattern recognition. Aiming at the problem of low micro-Doppler resolution under the condition of short dwell time, a sample missing signal reconstruction algorithm based on factor analysis (FA) model is proposed. Firstly, FA is used to describe the unknown complete signal, and then the mathematical model between the sample missing observation signal and the unknown complete signal is established. Then Bayesian theory is used to transform it into a full probability model. The model is solved by variable Bayesian expectation maximization (VBEM), so as to obtain the reconstruction of the complete signal. At the same time, for the problem of determining the number of FA factors, the automatic correlation determination (ARD) prior is introduced into the model to realize the automatic determination of the number of factors. Experimental results based on measured data show that the proposed method can achieve better reconstruction performance than the traditional compressive sensing (CS) method.
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