Abstract
A spatial filtering-based relevance vector machine (RVM) is proposed in this paper to separate coherent sources and estimate their directions-of-arrival (DOA), with the filter parameters and DOA estimates initialized and refined via sparse Bayesian learning. The RVM is used to exploit the spatial sparsity of the incident signals and gain improved adaptability to much demanding scenarios, such as low signal-to-noise ratio (SNR), limited snapshots, and spatially adjacent sources, and the spatial filters are introduced to enhance global convergence of the original RVM in the case of coherent sources. The proposed method adapts to arbitrary array geometry, and simulation results show that it surpasses the existing methods in DOA estimation performance.
Highlights
The problem of direction-of-arrival (DOA) estimation with sensor arrays is common in various applications, such as radar, sonar, and wireless communications [1,2,3,4], and the requirement for finding the directions of coherent sources widely emerges due to the factors like multipath and jamming
We carry out simulations to demonstrate the performance of the proposed method in coherent DOA estimation
Four other existing methods are listed for comparison, including spatial smoothing MUSIC [5], spatial filtering
Summary
The problem of direction-of-arrival (DOA) estimation with sensor arrays is common in various applications, such as radar, sonar, and wireless communications [1,2,3,4], and the requirement for finding the directions of coherent sources widely emerges due to the factors like multipath and jamming. Those methods combine the spatial sparsity of the incident signals when fitting the array output with an overcomplete model and show improved adaptation to the above mentioned demanding scenarios [8,9,10,11,12,13,14] As they realize DOA estimation by reconstructing the array output, instead of exploiting the covariance matrix, some of them perform well for correlated and coherent. Another technique for combining model sparsity with data fitting is the relevance vector machine (RVM) [16], and conclusions have been obtained to indicate that the RVM has identical global maximum as the L0-norm based function and greatly decreased local maxima if the signals are independent [15] It is even unimodal at high SNR when the basis amplitudes are highly scaled [15].
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