Target Parameter Estimation Of Compressive Sensing Radar Using Sensing Matrix Optimization

To solve the problem that the parameter estimation accuracy of Compressive Sensing Radar (CSR) degrades when the sensing matrix and target information vector are mismatching, a CSR target parameter estimation method using sensing matrix optimization is proposed in this paper. Firstly, a distance-Doppler 2-dimensions sparse sensing model of CSR in the case of sensing matrix mismatching is established, and then the sensing matrix is calibrated through performing iterative optimization on sensing matrix mismatching error and target information vector. Compared with conventional CSR target parameter estimation algorithms, the proposed method dramatically reduces the estimation error of distance-Doppler parameter, consequently improving the accuracy and robustness of CSR target parameter estimation. Finally, the effectiveness and superiority of the proposed method are proven through computer simulations.