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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Statistical Compressive Sensing and Feature Extraction of Time-Frequency Spectrum From Narrowband Radar
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Statistical Compressive Sensing and Feature Extraction of Time-Frequency Spectrum From Narrowband Radar

机译：窄带雷达时频谱的统计压缩传感和特征提取

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摘要

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.
机译：针对传统窄带系统的信号重建问题，我们提出了一种新的统计压缩感测（SCS）方法，实现了从损坏的时域测量来重建超级化时频谱。所提出的方法假设信号Obeys复杂高斯分布并开发分层贝叶斯模型。变分贝叶斯期望最大化（VBEM）用于对模型参数的后部分布进行推断。为了充分利用重建频谱的超级化特性，提取了一种新的超级化时频特征向量，以便随后的地面移动目标分类，即行走人和移动轮式车辆。测量数据的实验结果表明，所提出的重建方法可以获得良好的重建结果，超级化特征对人和车辆目标具有良好的分类性能。

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