Xiaofei Zhang

Sparsity-Based Two-Dimensional DOA Estimation for Coprime Array: From Sum–Difference Coarray Viewpoint

This paper addresses the issue of two-dimensional (2-D) direction of arrival (DOA) estimation with coprime planar arrays (CPPAs) via sparse representation. Our work differs from the partial spectral search approach [25], which suppresses the phase ambiguity by searching the common peaks of two subarrays. We focus on the coprime property of CPPA, where the sparse array extension model with sum–difference coarray (SDCA) is derived for larger degrees of freedom (DOFs). Besides, to optimize the selection of regularization parameter, we also construct a new sparse representation algorithm by estimating the errors between the signal and noise parts. Further, an iterative scheme is presented to transform the 2-D grids searching to several times of 1-D searching, where the initial values are obtained by extracting one difference coarray from SDCA. So the proposed method can achieve aperture extension, high estimation performance, and low computational complexity. Besides, the sparse array extension model for multiple-input multiple-output radars is discussed and the Cramér–Rao bound for 2-D DOA estimation with CPPA is also derived in detail. Finally, simulation results demonstrate the effectiveness of proposed method compared to the state-of-the-art methods.

Direction of Arrival Estimation in Low-Grazing Angle: A Partial Spatial-Differencing Approach

This paper addresses a partial spatial-differencing (PSD) approach for the direction of arrival estimation in a low-grazing angle (LGA) condition. By dividing the sample covariance matrix into several column subvectors, we first form the corresponding reconstructed subarray covariance matrices (RSCMs). We then calculate the spatial differencing matrix for the noise parts of RSCMs, while the non-noise parts are kept completely. That is, we build a PSD matrix. Compared with the existing spatial smoothing and full spatial-differencing methods, the PSD approach can use all the data information of the sample covariance matrix and also suppress the effect of additive white or colored noise more effectively. Simulation results show that our method provides a higher estimation accuracy and resolution than the state-of-the-art methods.

Sparse Extension Array Geometry for DOA Estimation With Nested MIMO Radar

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Propagator-based algorithm for localization of coherently distributed sources

O(N^{2})

An Angular Parameter Estimation Method for Incoherently Distributed Sources Via Generalized Shift Invariance

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DOA estimation of multiple sources for a moving array in the presence of phase noise

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Sum and difference coarrays based 2-D DOA estimation with co-prime parallel arrays

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Three-parallel co-prime array configuration for two-dimensional DOA estimation

O(N^{4})

Two dimensional angle estimation using separate nested acoustic vector sensor array

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A novel angular parameters estimator for incoherently distributed sources

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