Wideband DOA Estimation Based on Deep Residual Learning With Lyapunov Stability Analysis

Abstract

This letter puts forward a new method to estimate the direction of arrivals (DOAs) along with the source number under wideband circumstances. The method constructs a multiple-input-single-output (MISO) framework to make full use of the data from different frequency subbands. Each channel in the framework extracts angular features in parallel through stacked residual blocks. As a result, we can obtain an integrated output vector with smaller error. Because of the well-designed loss function, outputs of the trained model under the proposed framework are self-adjustable with the source number so that the regression of angle values and classification of signal numbers can be completed simultaneously. Moreover, we have proved herein that, if the equivalent matrices of residual blocks are negative definite, then the proposed framework is stable in the sense of Lyapunov. This stability is of great significance to practical applications. Numerical simulations on X-band data demonstrate high estimation accuracy and powerful generalization ability of the proposed scheme in the presence of nonuniform noise.