Exploiting Agile Waveforms and Sampling for Compressive Sensing Radar

Compressive Sensing (CS) has provided the radar community with a new mathematical framework for efficient and robust data collection and image formation. According to the theory of compressive sensing, a signal that is sparse in some domain can be recovered using far fewer samples than required by the Nyquist Sampling Theorem. Applications to radar were quick to emerge; the availability of high-resolution (traditional and synthetic aperture) radars that gather enormous amounts of data require faster, more efficient data processing algorithms to process the data. CS concepts have been applied to the radar imaging function; high range/velocity resolutions have been achieved using sufficiently smaller bandwidth than traditional radars. MIMO (multi-input, multi-output) radar has also provided the radar community with radar designs that achieve superior resolution compared to traditional systems having the same number of transmit and receive antennas. It is reasonable, therefore, to apply CS concepts to the design of MIMO radar systems. Since the direction of arrival (DOA) of targets approaching a radar system form a sparse vector in range-Doppler-angle space, compressive sensing concepts can be applied to the MIMO radar image formation and DOA estimation problems. SSCI is developing optimum radar transmit waveforms for CS/MIMO systems.