| Probing waveform synthesis and receive filter design play crucial roles in achievable performance for many radar applications. 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 auto-correlation properties, can reduce computational burden at the receiver and improve performance. Herein, we investigate various signal processing strategies to improve the performance of modern day radar systems. We highlight the interplay between waveform synthesis and receiver design.;We consider both single antenna systems (referred to as single-input single-output, or SISO, radar) as well as MIMO (multiple-input multiple-output) radar schemes. For SISO radar, we review a novel, cyclic approach to waveform design, and then compare the merit factors of these waveforms to other well-known sequences. Furthermore, we 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 show how these designs can significantly outperform conventional matched filter (MF) techniques for range compression.;We extend our discussion to include MIMO radar systems. We briefly highlight sequence set design, and we motivate the need for sequences with low auto- and cross-correlations. To further reduce clutter effects, we discuss receiver design for MIMO systems. We present a new least squares approach to target estimation. Additionally, we show how IAA can be extended to the MIMO case, both in the negligible and non-negligible Doppler cases. We present a new, regularized version of the algorithm designed to account for interferences outside of an angular region of interest. We provide a theoretical convergence analysis of IAA.;Finally, we consider MIMO transmit and receive beampattern design using sparse antenna arrays. We present a cyclic approach to beampattern design. Compared to a uniform linear array, we show that our sparse array design approach can, through larger degrees of freedom, better approximate desired transmit and receive beampatterns. |
