Real-Valued Variational Bayesian Inference for Direction-of-Arrival Estimation
Zheng Cao, Haoran Li, Haijun Fu · IEEE Sensors Letters · 2022
The existing real-valued methods for direction-of-arrival (DOA) estimation usually utilize the orthogonality of signal subspace and noise subspace to compress the dimension of the observation matrix, which, in return, jointly reduces the computational complexity and achieves a noise suppression effect. However, it could suffer a performance loss due to the inaccurately estimated signal subspace. To address this problem, we propose a novel real-valued variational Bayesian inference (VBI)-based method, where we consider the signal subspace matrix as a random variable and adaptively update it by exploiting its row-sparsity. It is challenging to perform the Bayesian inference under the coupling effect caused by the unknown signal subspace matrix. Therefore, a column-independent VBI factorization is further introduced into the VBI framework to decouple the signal of interest. Simulation results demonstrate that our method can significantly improve the DOA estimation performance.