CLUTTER-COMPENSATING ADAPTIVE WAVEFORMS WITH COGNITIVE RADAR USING ACTUAL TARGET MEASUREMENTS FOR TARGET CLASSIFICATION

Abstract

In this work, we investigate the target classification performance of a cognitive radar (CRr) using EM-simulated ground-based target responses and EM-measured aerial target responses in the presence of transmit waveform-dependent clutter. Moreover, we consider a more realistic target classification scenario where the cognitive radar is pointing at a look angle of 30°, in which clutter can definitely be a major interference. Previous works included EM-simulated target responses with an adaptive waveform design technique known as probability-weighted energy (PWE) for target recognition; however, practically measured power spectrum density (PSD) for signal-dependent clutter was not considered. Therefore, it is essential to build on prior works by considering signal-dependent clutter using measured target responses and a practical clutter model. We design clutter-compensating adaptive waveforms for CRr to improve classification in the presence of both narrowband and wideband clutter. Our results show improvement in classification performance of clutter-mitigating SNR and MI-based waveforms used in conjunction with PWE.