Effect of Sparse Array Geometry on Estimation of Co-array Signal Subspace

This paper considers the effect of sparse array geometry on the co-array signal subspace estimation error for Direction-of-Arrival (DOA) estimation. The second largest singular value of the signal covariance matrix plays an important role in controlling the distance between the true subspace and its estimate. For a special case of two closely-spaced sources impinging on the array, we explicitly compute the second largest singular value of the signal covariance matrix and show that it can be significantly larger for a nested array when compared against a uniform linear array with same number of sensors.

Gridless DOA Estimation for Minimum-Redundancy Linear Array in Nonuniform Noise

A gridless direction-of-arrival (DOA) estimation method to improve the estimation accuracy and resolution in nonuniform noise is proposed in this paper. This algorithm adopts the structure of minimum-redundancy linear array (MRA) and can be composed of two stages. In the first stage, by minimizing the rank of the covariance matrix of the true signal, the covariance matrix that filters out nonuniform noise is obtained, and then a gridless residual energy constraint scheme is designed to reconstruct the signal covariance matrix of the Hermitian Toeplitz structure. Finally, the unknown DOAs can be determined from the recovered covariance matrix, and the number of sources can be acquired as a byproduct. The proposed algorithm can be regarded as a gridless version method based on sparsity. Simulation results indicate that the proposed method has higher estimation accuracy and resolution compared with existing algorithms.

Novel Diagonal Reloading Based Direction of Arrival Estimation in Unknown Non-Uniform Noise

Nested array can expand the degrees of freedom (DOF) from difference coarray perspective, but suffering from the performance degradation of direction of arrival (DOA) estimation in unknown non-uniform noise. In this paper, a novel diagonal reloading (DR) based DOA estimation algorithm is proposed using a recently developed nested MIMO array. The elements in the main diagonal of the sample covariance matrix are eliminated; next the smallest MN-K eigenvalues of the revised matrix are obtained and averaged to estimate the sum value of the signal power. Further the estimated sum value is filled into the main diagonal of the revised matrix for estimating the signal covariance matrix. In this case, the negative effect of noise is eliminated without losing the useful information of the signal matrix. Besides, the degrees of freedom are expanded obviously, resulting in the performance improvement. Several simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

The application of high-resolution methods for DOA estimation using a linear antenna array

This paper presents results of direction of arrival (DOA) estimation using multiple signal classification (MUSIC), Root-MUSIC, and estimation of signal parameters via rotational invariance technique algorithms. As is well known, these algorithms are mainly based on the specific properties of the signal covariance matrix as well as the decomposition of the observation space into two subspaces, one for the signal and the other for the noise. Here, we are particularly interested in the quality of sources localization considering only the case of uncorrelated radio frequency signals impinging on an antenna array. A measurement system consisting of a linear array antenna and a five-port network applicable to a demodulator such as a receiver is used for the DOA estimation process. Co-simulations performed with the Advanced Device System and Matlab yielded interesting results not only on their performance but also on their limitation.

Two-Dimensional Direction-of-Arrival Estimation for Trilinear Decomposition-Based Monostatic Cross MIMO Radar

A low complexity monostatic cross multiple-in multiple-out (MIMO) radar scheme is proposed in this paper. The minimum-redundancy linear array (MRLA) is introduced in the cross radar to improve the efficiency of the array elements. The two-dimensional direction-of-arrival (DOA) estimation problem links to the trilinear model, which automatically pairs the estimated two-dimensional angles, requiring neither eigenvalue decomposition of received signal covariance matrix nor spectral peak searching. The proposed scheme performs better than the uniform linear arrays (ULA) configuration under the same conditions, and the proposed algorithm has less computational complexity than that of multiple signal classification (MUSIC) algorithm. Simulation results show the effectiveness of our scheme.