Compressive sensing in radar sensor networks using pulse compression waveforms

Inspired by recent advances in compressive sensing (CS), we introduce CS to the radar sensor network (RSN) using the pulse compression technique in order to efficiently compress, restore and recover the radar data. For the sake of simplicity but without losing generality, we study an RSN consisting of a number of transmit sensors and one receive sensor. Our idea is to employ a set of Stepped-Frequency waveforms as pulse compression codes for transmit sensors, and to use the corresponding Stepped-Frequency (SF) waveforms as the sparse matrix in the receive sensor due to the orthogonality of the basis. 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. In addition, a diversity gain could also be obtained at the output of the set of the matched filters in the receive sensor. Simulation results show that even if the signal could not be perfectly reconstructed, the probability of miss detection of target could be kept zero. In the future, the reconstructed signal would further be applied to the study of target recognition.