一种稳健的盲稀疏度压缩感知雷达目标参数估计方法

发布时间：2018-05-05 19:31

本文选题：压缩感知雷达(CSR) + 盲稀疏度　；参考：《电子与信息学报》2014年04期

【摘要】：针对压缩感知雷达(Compressive Sensing Radar,CSR)在感知矩阵和目标信息矢量失配时距离-多普勒参数估计性能下降的问题,该文提出一种稳健的盲稀疏度CSR目标参数估计方法。首先建立了CSR系统模型失配时的距离-多普勒2维参数稀疏感知模型,推导了以最小化感知矩阵相干系数(Coherence of Sensing Matrix,CSM)为准则的波形优化目标函数。其次提出了一种新的盲稀疏度CSR目标参数估计方法,通过发射波形,系统模型失配误差和目标信息矢量的相互迭代,逐步校正系统感知矩阵,最终以较高精度估计目标距离-多普勒参数。与传统CSR目标参数估计方法相比,该方法显著降低了CSR系统距离-多普勒参数的估计误差,改善了CSR目标参数估计的准确性和鲁棒性。计算机仿真验证了该方法的有效性。
[Abstract]:A robust blind sparse Sensing (CSR) parameter estimation method is proposed to solve the problem that the performance of range-Doppler parameter estimation of compressed sensing radar is degraded when the perceptual matrix and target information vector mismatch. In this paper, the range-Doppler parameter sparse sensing model for CSR system model mismatch is established, and the waveform optimization objective function based on the criterion of minimizing coherence of Sensing matrix is derived. Secondly, a new blind sparse degree CSR target parameter estimation method is proposed. By transmitting waveform, system model mismatch error and target information vector iteration, the system perception matrix is corrected step by step. Finally, the range-Doppler parameters are estimated with high accuracy. Compared with the traditional CSR target parameter estimation method, this method significantly reduces the range Doppler parameter estimation error of CSR system, and improves the accuracy and robustness of CSR target parameter estimation. The effectiveness of the method is verified by computer simulation.
【作者单位】：南京理工大学电子工程与光电技术学院;中国空间技术研究院微波遥感与数传技术研究所;
【分类号】：TN958

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