Application of STFT–NGRC Joint Algorithm for Doppler Through-Wall Radar Target Tracking

Estimation accuracy of target Instantaneous Frequency (IF) significantly affects the tracking performance of a through wall radar (TWR). In this paper, a novel theoretical approach to estimate IF of human targets is proposed with a united application of the short-time Fourier transform (STFT) and a modified next-generation reservoir computing (NGRC) system, which can be very promising in real-time human tracking applications. For this approach, the STFT is first applied to identify aliasing areas of radar echoes. Then, a NGRC dynamic learning system with adaptive error compensation processing is proposed for target IF prediction on these aliasing areas. Finally, the NGRC output weights are updated and the prediction process is iteratively performed until the IF estimation results of all the aliasing intervals are acquired. Compared with traditional methods based on fixed output weights throughout the entire prediction process, the proposed approach can more effectively suppress the cumulative errors and improve the target tracking performance of a TWR. A series of experimental measurements are presented as a preliminary assessment of the proposed approach.