Signal Covariance Matrix Optimization for Transmit Beamforming in MIMO Radars

2007 ◽  
Author(s):  
Tuomas Aittomaki ◽  
Visa Koivunen
Keyword(s):  
Covariance Matrix ◽  
Transmit Beamforming ◽  
Matrix Optimization ◽  
Signal Covariance Matrix

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

2021 ◽  
Vol 35 (11) ◽  
pp. 1435-1436
Author(s):  
Mehmet Hucumenoglu ◽  
Piya Pal
Keyword(s):  
Covariance Matrix ◽  
Estimation Error ◽  
Doa Estimation ◽  
Singular Value ◽  
Signal Subspace ◽  
Sparse Array ◽  
Subspace Estimation ◽  
Signal Covariance Matrix ◽  
Special Case ◽  
Array Geometry

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.

Transmit Signal Design in Colocated MIMO Radar Without Covariance Matrix Optimization

2017 ◽  
Vol 53 (5) ◽  
pp. 2178-2186 ◽  
Author(s):  
Sadjad Imani ◽  
Mohammad Mahdi Nayebi ◽  
Seyed Ali Ghorashi
Keyword(s):  
Covariance Matrix ◽  
Mimo Radar ◽  
Signal Design ◽  
Matrix Optimization

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

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Hao Zhou ◽  
Guoping Hu ◽  
Junpeng Shi ◽  
Ziang Feng
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Main Diagonal ◽  
Sample Covariance ◽  
Uniform Noise ◽  
Negative Effect ◽  
Signal Covariance Matrix

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.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Changyun Qi ◽  
Gong Zhang ◽  
Jiawen Yuan
Keyword(s):  
Covariance Matrix ◽  
Linear Array ◽  
Estimation Method ◽  
Doa Estimation ◽  
Residual Energy ◽  
Estimation Accuracy ◽  
Two Stages ◽  
Signal Covariance Matrix ◽  
Toeplitz Structure ◽  
True Signal

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.

SINR Enhancement in Colocated MIMO Radar Using Transmit Covariance Matrix Optimization

2017 ◽  
Vol 24 (3) ◽  
pp. 339-343 ◽  
Author(s):  
Mohamad Haghnegahdar ◽  
Sadjad Imani ◽  
Seyed Ali Ghorashi ◽  
Esfandiar Mehrshahi
Keyword(s):  
Covariance Matrix ◽  
Mimo Radar ◽  
Matrix Optimization
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