Compressed Sensing in Inverse Synthetic Aperture Imaging Lidar

摘要

The inverse synthetic aperture imaging lidar (ISAIL) possesses great potential for ultra-high-resolution imaging and accurate recognition of moving targets owing to the ultra-high frequency and ultra-wide band of the laser signal. A major challenge of the signal processing in ISAIL is the requirement for even unpractical high sampling rate. In this paper, we introduce the compressed sensing (CS) theory to ISAIL for the sake of reducing the sampling rate of echoes. The linear frequency-modulated (LFM) echoes are dechirped by multiplying with the reference signal and filtered using an analog lowpass filter, and then the CS sampling processing is implemented. Simulations demonstrate that high-quality images can be reconstructed even though the sampling rate is much lower than the Nyquist rate.