Aiming at the signal reconstruction problem for the conventional narrowband radar system, we propose a new statistical compressive sensing (SCS) method to achieve the reconstruction of superresolution time-frequency spectrum from the corrupted time-domain measurement. The proposed method assumes that the signal obeys complex Gaussian distribution and develops a hierarchical Bayesian model. Variational Bayesian expectation maximization (VBEM) is used to perform inference for the posterior distributions of the model parameters. In order to fully exploit the superresolution characteristics of reconstructed spectrum, a novel superresolution time-frequency feature vector is extracted for subsequent classification of ground moving targets, i.e., walking person and a moving wheeled vehicle. Experimental results on measured data show that the proposed reconstruction method can obtain good reconstruction results and the superresolution feature has good classification performance for human and vehicle targets.

中文翻译：

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窄带雷达时频频谱的统计压缩感知与特征提取

针对传统窄带雷达系统的信号重建问题，我们提出了一种新的统计压缩感知（SCS）方法，以实现从损坏的时域测量中重建超分辨率时频频谱。所提出的方法假设信号服从复杂的高斯分布并开发了一个分层贝叶斯模型。变分贝叶斯期望最大化 (VBEM) 用于对模型参数的后验分布进行推理。为了充分利用重构频谱的超分辨特性，提取了一种新颖的超分辨时频特征向量，用于后续地面运动目标的分类，即行走的人和移动的轮式车辆。