A Robust Blind Sparsity Target Parameter Estimation Algorithm for Compressive Sensing Radar
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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。
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关键词:
- 压缩感知雷达(CSR) /
- 盲稀疏度 /
- 感知矩阵相干系数(CSM) /
- 模拟退火(SA)算法
Abstract: In order to enhance the performance of estimating range-Doppler parameters in presence of mismatch error between sensing matrix and target information vector for Compressive Sensing Radar (CSR), a robust blind sparsity target parameter estimation algorithm is proposed. First, a two-dimensional sparse sensing model for range-Doppler estimation is established when there exists CSR system model mismatch error, and a waveform optimization object function is derived based on minimization Coherence of Sensing Matrix (CSM). Then, a novel blind sparsity CSR algorithm is employed to correct system sensing matrix and estimate the range-Doppler parameters by optimizing iteratively transmit waveform, system mismatch error and target information vector. Compared with traditional CSR algorithm, the proposed method reduces the range-Doppler estimation error, and enhances the accuracy and robustness of CSR target information estimation. The validity of the proposed method is demonstrated with numerical simulation. -
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