Time-frequency DOA estimation of chirp signals based on multi-subarray

Spatial time-frequency distribution (STFD) utilizes the spatial time-frequency characteristics of chirp signals and effectively improves the direction of arrival (DOA) estimation performance. However, the existing methods based on the STFD matrix assumed that each source has good time-frequency point selection performance. In practice, the time-frequency point selection accuracy of chirp signals suffers from the signal-to-noise ratio (SNR). Especially in the case of low SNR, the time-frequency point selection error increased, leading to the reduction of the SNR and degradation of the DOA estimation performance in the time-frequency domain. To solve the above problems, we propose a time-frequency DOA estimation method based on multiple subarrays. The array is firstly divided into several overlapping subarrays, and the data received by the array is transformed from the element space into beamspace by using beamforming, which improves the source separation and the SNR. Then, chirp signals possess the ideal energy collection features in the time-frequency domain so that the time-frequency analysis tools are used for separated sources in beamspace to further improve the source separation and the SNR in the time-frequency domain. This results in better time-frequency point selection accuracy of chirp signals at a low SNR regime. Finally, the averaged STFD matrix can be obtained through averaging over multiple single-source time-frequency points in the time-frequency domain, and the DOAs can be obtained by combining with the subspace-based method. The theoretical analysis and simulation results indicate that compared with the existing STFD-based methods, the proposed method in this paper provides good performance on estimation and resolution in cases with low input SNRs due to beamspace processing. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation.