Memory-Augmented Autoencoder-Based Nonhomogeneous Detector for Airborne Radar Space-Time Adaptive Processing

For airborne radar space-time adaptive processing (STAP), conventional statistic-based nonhomogeneous detectors (NHDs) can help to eliminate the range cells contaminated by outliers, while their performance usually depends on the available training range cells. To solve this problem, the nonhomogeneous detection problem is transformed to an out-of-distribution (OOD) data detection problem, and a deep learning (DL)-based NHD is then proposed in this study. According to the reconstruction errors of their corresponding radar signals in a trained complex-valued memory-augmented autoencoder (CV-MemAE), the DL-NHD can detect and eliminate the contaminated range cells and thus improve the performance of STAP effectively. Simulation results show that, compared with three existing NHDs, the proposed DL-NHD can detect outlier-contaminated range cells with zero miss detection and zero false detection. After using DL-NHD to eliminate the outlier-contaminated range cells, the STAP output signal-to-clutter-plus-noise-ratio (SCNR) loss can be improved by about 10–20 dB.