Waveform Optimization of High SNR and High Resolution Cognitive Radar for Sparse Target Detection

Abstract

The maximum SNR detection can be achieved by selecting the frequency band with the greatest difference of target response compared with the ambient clutter or noise. However, compared with the traditional LFM signal, the optimized cognitive waveform has a narrower bandwidth, so the target resolution obtained after matched filtering will be reduced correspondingly, which will cause difficulties for the later fine recognition task. Target feature in this paper, combined with sparse optimization of high SNR high-resolution cognitive radar waveform design method for the target in the scene, in cognitive radar high SNR radar waveform optimization, on the basis of sparse frequency emission waveform design, with the method of sparse reconstruction recovery goals, can achieve the same resolution with linear frequency modulation signal bandwidth, at the same time as the target signal is sparse form, signal-to-noise ratio was improved greatly, compared with the traditional waveform goals at the same time improve the signal-to-noise ratio and resolution. On the basis of theoretical results, cognitive sparse waveforms are designed for sparse targets with known response functions by combining with actual signals such as finite time and constant envelopment to generate constraints. Simulation results show that this method can obtain high SNR while obtaining high resolution targets.