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。
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.
出处
《电子与信息学报》
EI
CSCD
北大核心
2014年第4期960-966,共7页
Journal of Electronics & Information Technology