University of Teramo
Inspired by recent advances in Compressive Sensing (CS), the authors 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, they study an RSN consisting of a number of transmit sensors and one receive sensor. Their 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. They 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.