High resolution range-reflectivity estimation of radar targets via compressive sampling and Memetic Algorithm

Recent results of Compressive Sampling (CS) have demonstrated its feasibility in high-resolution radar targets estimation and imaging . However, the signal recovery is reduced to seeking a sparse solution to an underdetermined linear system of equations. It is potentially very difficult because even finding a solution that approximates the true minimum is NP-hard. In this paper, we introduce Memetic Algorithm (MA) to solve this non-convex l0-norm minimization problem, and design a compressive receiver for high-resolution range-reflectivity estimation of multiple radar targets. A double-population MA is proposed, where the position population is used to evaluate the ranges, and the coefficient population is used to realize a local search of target reflectivities. By combining the global search with a local searching operation to exploit the available knowledge in the recovery, the proposed MA outperforms the general purpose optimization algorithms in terms of the quality of solution. Some experiments are taken to investigate the performance of this compressive receiver at different sampling rates, and the results show the superiority to its counterparts in both noiseless environment and noisy, cluttered environment.