Sub-Nyquist Spectrum Sensing and DOA Estimation with Space-Time Trilinear Modeling

With the development of radar, wireless communications, remote sensing, and other fields, the signal bandwidth is getting wider. Traditional spectrum sensing and direction of arrival (DOA) estimation based on Nyquist sampling leads to a high sampling rate, which poses challenges to signal acquisition and processing equipment. In this article, a novel sub-Nyquist spectrum sensing and DOA estimation method based on the space-time trilinear model is proposed to improve the performance, and also solve the rank deficiency problem in the presence of coherent signals. First, based on a uniform linear array (ULA) modulated wideband converter (MWC) sub-Nyquist sampling system, a space-time trilinear model constructed directly by sub-Nyquist sampling is proposed which can make full use of the spatial and temporal multidimensional information of the signal simultaneously to achieve better performance. Compared to previous works, it avoids the computation of correlation matrices or signal subspaces, so the estimation is not affected by the nonideal statistical properties of finite snapshots. Then, based on the proposed space-time trilinear model, we provide a simple extension to overcome the rank deficiencies associated with coherent signals by exploiting the spatial phase differences between subarrays, while improving the robustness of the system. The effectiveness of the proposed method is verified by simulation and hardware experiments.