Compressed PARAFAC Model-based Two-Dimensional Angle Estimation for Acoustic Vector-Sensor Arrays

摘要

In this paper, in order to estimate the angles for arbitrarily spaced arrays with acoustic vector-sensor, we combine the compressed sensing theory with parallel factor (PARAFAC) model, and propose a neoteric angle estimation algorithm. The proposed algorithm firstly compressed the PARAFAC model, then exploit trilinear alternating least square (TALS) algorithm to estimate the parameter matrices and obtains the angle estimation. Owing to compression, the proposed algorithm has smaller storage requirement and lower computational complexity, compared with the conventional PARAFAC algorithm. It's also works well to achieve automatically paired azimuth and elevation angles. The angle estimation performance of the proposed algorithm is close to the conventional PARAFAC algorithm, and is better than the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Various simulation results demonstrate the effectiveness of our algorithm.