DoA and Bandwidth Estimation of Unknown Signals Based on MT-BCS through Multiple Snapshots Data

Based on the multi-task Bayes Compressive Sensing (MT-BCS), a Direction-Of-Arrival (DOA) and bandwidth (BW) estimation strategy impinging on a linear array using multiple snapshots data is proposed. The DOA estimation is using as the reconstruction of sparse signal constrained by the Laplace prior through multi-task Bayes Compressive Sensing. Receiving wideband signal data through linear array, and the space is divided into I parts according to the equal interval. The data of interest are assumed to be represented as I-dimensional vector S, the wideband signal can be reconstructed accurately using only a small number. The receiving antenna operates in the frequency range [f min , f max ]. Starting from the voltages measured at the output of the array elements at a multiple time instant at f p = f min + ∆f, p = 1,…, P, the retrieval of the DoAs is addressed by means of a customized strategy based on MT-BCS in order to correlate the solutions obtained over different frequency samples. The bandwidth of the signals is obtained as a by product by identifying at which frequencies (f p , p = 1,…, P) the MT-BCS estimations include a signal along the i-th (i = 1,…, I) sampling direction. From the outputs of different frequency we can know the DoA and BW of signals. A preliminary numerical result is reported to show the behavior of the proposed approach in different snapshots and noise, and also using RMSE to present numerical results more clearly.