Abstract:

In practical application, the mismatch between measurement vector and sensing matrix caused by completely perturbed observations will result in a sharp decline in the performance of parameter estimation for compressed sensing radar (CSR). A novel robust parameter estimation algorithm is proposed based on Bayesian compressed sensing (BCS). The completely perturbed sparse linear model is firstly built, and the robust target function is derived with the maximum a posteriori (MAP) of the sparse vector when the completely perturbed matrix obeys Cauchy distribution. Then the optimal solution is achieved through the alternate iteration between the sparse vector and the scale parameter. Compared with most existing recovery algorithm and their derivants, the proposed method effectively improves the robustness against the foregoing mismatch, increases the target detection probability and reduces the estimation error. The effectiveness of the proposed algorithms is demonstrated by computer simulations.