Probing waveform synthesis and receive filter design for active sensing systems

Probing waveform synthesis and receive filter design play crucial roles in achievable performance for active sensing applications, including radar, sonar, and medical imaging. We focus herein on conventional single-input single-output (SISO) radar systems. A flexible receive filter design approach, at the costs of lower signal-to-noise ratio (SNR) and higher computational complexity, can be used to compensate for missing features of the probing waveforms. A well synthesized waveform, meaning one with good autocorrelation properties, can reduce computational burden at the receiver and improve performance. Herein, we will highlight the interplay between waveform synthesis and receiver design. We will review a novel, cyclic approach to waveform design, and then compare the merit factors of these waveforms to other well-known sequences. In our comparisons, we will consider chirp, Frank, Golomb, and P4 sequences. Furthermore, we will overview several advanced techniques for receiver design, including data-independent instrumental variables (IV) filters, a data-adaptive iterative adaptive approach (IAA), and a data-adaptive Sparse Bayesian Learning (SBL) algorithm. We will show how these designs can significantly outperform conventional matched filter (MF) techniques for range compression as well as for range-Doppler imaging.