Air Vehicle Target Recognition with Jammer Nulling Adaptive Waveforms in Cognitive Radar Using High-Fidelity RCS Responses

In this paper, we introduce two new jammer nulling transmit-adaptive waveforms applied to cognitive radar (CRr) framework for air vehicle (or aircraft) target response recognition in the presence of narrowband jammer noise and AWGN. The jammer nulling adaptive waveforms function as a countermeasure against narrowband jamming signals that disrupts the CRr platform's ability to classify the targets accurately when utilizing jammer-uncompensated adaptive waveforms. The jammer nulling waveforms are derived based on optimizing signal-to-interference-plus-noise-ratio (SINR) and mutual information (MI) metrics; while the adaptive waveforms are obtained by the jammer incorporated probability-weighted energy (PWE) or probability-weighted spectral density (PWSD) method. High-fidelity EM simulated radar cross section (RCS) responses of aircraft targets are utilized to investigate the performance of jammer nulling adaptive waveforms. The results from simulations show an improvement in jamming nulling SINR and MI-based waveforms' classification performance over their jammer-uncompensated counterparts and the receive-adaptive wideband pulsed waveform. The increase in classification performance depends on the target frequency responses and the narrowband jammer spectrum profile.