Abstract
In the field of array signal processing, distributed sources can be regarded as an assembly of point sources within a spatial distribution. In this study, a two-dimensional (2D) non-symmetric incoherently distributed (ID) source model is proposed; we explore the estimation of a 2D non-symmetric ID source using L-shape arrays. The 2D non-symmetric ID source is established by modeling the angular power density function (APDF) as a Gaussian mixture model. Estimation of the non-symmetric distributed source is proposed based on the expectation maximization (EM) framework. The proposed EM iterative framework contains three steps in the process of each circle. Firstly, the nominal azimuth and nominal elevation of each Gaussian component are obtained from the phase parts of elements in sample covariance matrices. Then the angular spreads can be solved through a one-dimensional (1D) search by the original generalized Capon estimator. Finally, weights of each Gaussian component are obtained by solving the least-squares estimator. Simulations are conducted to verify the effectiveness of the estimation technique.
Highlights
IntroductionApplications such as wireless communications, radar and underwater acoustics, point source models (assuming that signals impinging into receive arrays are from point sources) are commonly used, which can simplify calculations
In array signal processing, applications such as wireless communications, radar and underwater acoustics, point source models are commonly used, which can simplify calculations
As the principle that symmetric distributions can form a non-symmetric distribution is true in the case of 2D, we present a 2D non-symmetric incoherently distributed (ID) source model by constituting angular power density function (APDF) with the 2D
Summary
Applications such as wireless communications, radar and underwater acoustics, point source models (assuming that signals impinging into receive arrays are from point sources) are commonly used, which can simplify calculations. In the real surroundings of radar and sonar systems, because of multipath propagation between receive arrays and targets, especially when the distances of targets and receive arrays are short, the spatial scatterers of targets cannot be ignored, assumptive condition of point source is no longer valid and point source models cannot characterize sources effectively, which should be described by distributed source models [1]. The shape of spatial distribution is related to geometry and surface property of a target, for instance, in underwater detection. Considering multipath propagation and the surface feature of targets, distributed source models are more appropriate in near field of radar or sonar detection.
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