Cognitive MIMO Imaging Radar Based on Doppler Filtering Waveform Separation

The problem of waveform separation for the multiple-input multiple-output (MIMO) imaging radar is considered in this article. In the conventional match filtering (MF)-based waveform separation methods, the cross-correlation noise among different waveforms is inevitable and may seriously reduce the performance of the MIMO radar, especially in target imaging, where a large number of transmissions are required. In this article, a cognitive waveform separation method is proposed based on the orthogonality of the frequency-stepped signals in the Doppler domain. The basic idea of the proposed waveform separation method is to extract different spectrums that occupy different Doppler coverages after interpulse phase modulation. In particular, the mix operation before filtering is conducted due to its compression effort to hold all the Doppler spectrums in one period. Depending on the prior knowledge, the target characteristics, and the imaging resolution requirement, a cognitive optimization model considers both the waveform separation and the target imaging to obtain the most suitable transmitting parameters for different targets. The inner relationships between the transmitting parameters, waveform separation, and radar imaging are fundamental to transmitting the parameter optimization. The perception-action cycle of cognition is achieved via the quantum genetic algorithm (QGA). The simulation results show the superiority of the proposed method with respect to the MF-based separation methods in the MIMO radar imaging.