Sparsity-Based Underdetermined DOA Estimator via Toeplitz Matrix Reconstruction for Sparse Array

Author(s):  
Wang Geng ◽  
He Yi ◽  
Liu Kang ◽  
Chen Changxiao ◽  
Feng Mingyue ◽  
...  
2011 ◽  
Vol 135-136 ◽  
pp. 331-335
Author(s):  
Ling Tang ◽  
Hong Song ◽  
Lian Jun Hu

A new blind beamforming algorithm for separating and estimating coherent signals arriving at an antenna array is proposed in this paper. This algorithm is implemented through arranging the received data’s correlation function of each array element and reference array element (the first array element), to form the Hermitian Toeplitz matrix first. Then through the singular value decomposition of the matrix the signal subspace and noise subspace can be get, in order to achieve the direction vector of coherent sources to beamform, which can separate the independent signals from different directions effectively without knowing the transcendent knowledge of the signals. The new approach can estimate the coherent signals’ direction-of-arrival(DOA) on the basis of separation, and has good performance under the low SNR. The simulation results show that the proposed method is effective.


Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 707 ◽  
Author(s):  
Zhen Meng ◽  
Weidong Zhou

Coprime arrays have shown potential advantages for direction-of-arrival (DOA) estimation by increasing the number of degrees-of-freedom in the difference coarray domain with fewer physical sensors. In this paper, a new DOA estimation algorithm for coprime array based on the estimation of signal parameter via rotational invariance techniques (ESPRIT) is proposed. We firstly derive the observation vector of the virtual uniform linear array but the covariance matrix of this observation vector is rank-deficient. Different from the traditional Toeplitz matrix reconstruction method using the observation vector, we propose a modified Toeplitz matrix reconstruction method using any non-zero row of the covariance matrix in the virtual uniform linear array. It can be proved in theory that the reconstructed Toeplitz covariance matrix has full rank. Therefore, the improved ESPRIT method can be used for DOA estimation without peak searching. Finally, the closed-form solution for DOA estimation in coprime array is obtained. Compared to the traditional coprime multiple signal classification (MUSIC) methods, the proposed method circumvents the use of spatial smoothing technique, which usually results in performance degradation and heavy computational burden. The effectiveness of the proposed method is demonstrated by numerical examples.


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