Improved compressed sensing radar by fusion with matched filtering

Compressed Sensing (CS) provides a rich mathematical framework to efficiently acquire a sparse signal from few non-adaptive measurements. In radar imaging, most scenes are sparse and CS can be successfully applied for efficiently acquiring the target scene. Although the use of CS in radar is advantageous in many aspects, a higher noise in the received signal makes the output of CS unreliable. We propose a framework based on CS and matched filtering to improve the performance of CS particularly in high noise scenarios. We realize this framework by CS on chirp signal and discuss some limitations associated with it. Numerical experiments confirm a substantial performance improvement using the proposed framework compared to conventional CS reconstruction.