In vehicular applications, the radar technology offers excellent resolvability and immunity to bad weather conditions in comparison to visible and infrared imaging techniques. However, since typical radar bandwidth is in tens of Giga Hertz, the cost overhead of ultra-high frequency radar signal processors is excessive which limits a mass deployment of radar-based advanced vehicular safety features. Addressing this limitation, we pursue a suite of novel signal processing tools for automotive sensing that are constrained to operate on low-cost hardware, and yet, guarantee a high fidelity and real-time sensing—a perfect fusion of effective radar signal processing algorithms and hardware.
Waveform processing and design for radar have been of interest to engineers, system theorists and mathematicians in the last couple of decades. In the last decade, however, the radar world has been revolutionized by a significant increase in computational resources; an ongoing revolution with considerable momentum. Such advances are enabling waveform design and processing schemes that can be adaptive (also referred to as cognitive, or smart) while being extremely agile in modifying information collection strategy based on new measurements, and/or modified target or environmental parameters. These novel design and processing schemes have also opened new avenues for enhancing robustness in radar detection/estimation, as well as coexistence in networked environments with limited resources such as a shared spectrum—all leading to increased reliability.