Clutter-compensating Adaptive Waveforms with Cognitive Radar for Target Classification Using EM-simulated Ground-based RCS Responses

We investigate the ground-based classification performance of a cognitive radar (CRr) using high-fidelity target models with their RCS responses in the presence of transmit waveform-dependent clutter. Moreover, we consider a proper grazing angle for clutter scenarios. The CRr utilizes real-time adaptive waveform technique called probability-weighted energy (PWE) technique. In this work, the radar tries to classify any of the four ground-based vehicles at a look angle of 30^◦ where clutter can definitely be a major interference. As such, we design clutter-compensating adaptive waveforms for CRr to improve classification in the presence of both narrowband and wideband clutter.