Abstract
Based on the assumption that only a few point sources exist in the spatial spectrum, the direction-of-arrival (DOA) estimation problem can be formulated as a problem of sparse representation of signal with respect to a dictionary. By choosing a proper dictionary, the array measurements can be well approximated by a linear combination of a few entries of the dictionary, in which the non-zero elements of the sparse coefficient vector correspond to the targets’ arrival direction. Conventionally, the desired sparsity of signal is guaranteed by imposing a constraint of Laplace prior on the distribution of signal. However, its performance is not satisfied under the condition of insufficient data or noisy environment since a lot of false targets will appear. Considering that the Meridian distribution has the characteristic of high energy concentration, we propose to adopt the Meridian prior as the prior distribution of the coefficient vector. Further, we present a new minimization problem with the Meridian prior assumption (MMP) for DOA estimation. Because the Meridian prior imposes a more stringent constraint on the energy localization than the Laplace prior, the proposed MMP method can achieve a better DOA estimation, which is embodied in higher resolution and less false targets. The experiments of both simulation and ground truth data process exhibit the superior performance of our proposed algorithm.
Highlights
In many fields, including radar, sonar, and medical signal processing, one of the most highly explored research problems is how to determine the precise direction-of-arrival (DOA) of multiple incident signals from noisy measurements of a sensor array
It is unable to separate the closely spaced sources, when the angular interval is smaller than the Rayleigh resolution limit
A sparse signal is known as a localized energy signal [7], in which zero-value entries will be
Summary
In many fields, including radar, sonar, and medical signal processing, one of the most highly explored research problems is how to determine the precise direction-of-arrival (DOA) of multiple incident signals from noisy measurements of a sensor array. It is unable to separate the closely spaced sources, when the angular interval is smaller than the Rayleigh resolution limit. This resolution limitation can be overcome by some high-resolution DOA estimation algorithms [4-6], in which a representative class is the subspace-based method. The popular constraint of minimizing l1-norm does not fully exploit the sparsity of the coefficient vector, especially under the condition that the number of point sources increases or/and the number of array measurements decreases. Inspired by the previous work, in this paper, we impose a Meridian prior [11] as the sparsity constraint for the spatial spectrum and develop a new minimization problem with the Meridian prior assumption (MMP) to achieve high-resolution DOA estimation for the point source scene.
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