Compressive sensing in radar sensor networks for target RCS value estimation

Recently, there are a growing interest in the study of compressive sensing (CS). In this paper, we introduce CS to radar sensor network (RSN) within the pulse compression technique in order to efficiently compress, restore and then reconstruct the radar data. We employ a set of Stepped-Frequency waveforms as pulse compression codes for transmit sensors, and to use the same set of Stepped-Frequency (SF) waveforms as the sparse matrix for each receive sensor. We conclude that the signal samples along the time domain could be largely compressed so that they could be recovered by a small number of measurements which depend on the number of transmit sensors. In addition, we develop a Maximum Likelihood (ML) Algorithm for radio cross section (RCS) parameter estimation and provide the Cramer-Rao lower bound (CRLB) to validate the theoretical result. We also provide simulation results illustrating that the variance of RCS parameter estimation θ satisfies the CRLB and our ML estimator is an accurate estimator on the target RCS parameter.