Photonic direction-of-arrival estimation based on compressive sensing

The optical approach to estimate the direction-of-arrival (DOA) estimation of microwave signals has attracted a lot of attention recently. Most of existing methods are based on a one-sensor array system, which converts the DOA estimation problem into an optical power estimation problem. Their main disadvantage is that additional work is needed to represent the relationship between the phase shifts and the optical powers before estimation. The algorithm proposed in this paper is based on an N-sensor array (N>2), where the optical power vector is obtained first and then a compressive sensing based approach is applied to directly estimate the phase shift. As demonstrated by simulation results, the proposed algorithm can achieve much better estimation accuracy and robustness than existing optical DOA estimation algorithms considered

Wideband DOA Estimation with Frequency Decomposition via a Unified GS-WSpSF Framework

A unified group sparsity based framework for wideband sparse spectrum fitting (GS-WSpSF) is proposed for wideband direction-of-arrival (DOA) estimation, which is capable of handling both uncorrelated and correlated sources. Then, by making four different assumptions on a priori knowledge about the sources, four variants under the proposed framework are formulated as solutions to the underdetermined DOA estimation problem without the need of employing sparse arrays. As verified by simulations, improved estimation performance can be achieved by the wideband methods compared with narrowband ones, and adopting more a priori information leads to better performance in terms of resolution capacity and estimation accuracy

Underdetermined Wideband DOA Estimation of Off-Grid Sources Employing the Difference Co-Array Concept

A wideband off-grid model is proposed to represent dictionary mismatch under the compressive sensing framework exploiting difference co-arrays. A group sparsity based off-grid method is proposed for underdetermined wideband direction of arrival (DOA) estimation which provides improved performance over the existing group sparsity based method with a same search grid. A two-step approach is then proposed which achieves an even better performance with significantly reduced computational complexity