Improved joint direction of arrival and frequency estimation using propagator method

2010 ◽  
Author(s):  
Xulingyun ◽  
Xiaofei Zhang ◽  
Xu Zongze
Keyword(s):  
Frequency Estimation ◽  
Direction Of Arrival ◽  
Propagator Method ◽  
Joint Direction

scholarly journals Improved ESPRIT Method for Joint Direction-of-Arrival and Frequency Estimation Using Multiple-Delay Output

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Xudong ◽  
Xiaofei Zhang ◽  
Jianfeng Li ◽  
Jinchao Bai
Keyword(s):  
Antenna Arrays ◽  
Frequency Estimation ◽  
Direction Of Arrival ◽  
Joint Angles ◽  
Linear Arrays ◽  
Estimation Performance ◽  
Multiple Delay ◽  
Joint Direction ◽  
Esprit Algorithm ◽  
Linear Antenna Arrays

An automatic pairing joint direction-of-arrival (DOA) and frequency estimation is presented to overcome the unsatisfactory performances of estimation of signal parameter via rotational invariance techniques- (ESPRIT-) like algorithm of Wang (2010), which requires an additional pairing. By using multiple-delay output of a uniform linear antenna arrays (ULA), the proposed algorithm can estimate joint angles and frequencies with an improved ESPRIT. Compared with Wang’s ESPRIT algorithm, the angle estimation performance of the proposed algorithm is greatly improved. The frequency estimation performance of the proposed algorithm is same with that of Wang’s ESPRIT algorithm. Furthermore, the proposed algorithm can obtain automatic pairing DOA and frequency parameters, and it has a comparative computational complexity in contrast to Wang’s ESPRIT algorithm. By the way, this proposed algorithm can also work well for nonuniform linear arrays. The useful behavior of this proposed algorithm is verified by simulations.

Joint DOA and Frequency Estimation for Linear Array with Compressed Sensing PARAFAC Framework

2017 ◽  
Vol 26 (09) ◽  
pp. 1750136 ◽  
Author(s):  
Shu Li ◽  
Zezhou Sun ◽  
Xiaofei Zhang ◽  
Weiyang Chen ◽  
Dazhuan Xu
Keyword(s):  
Compressed Sensing ◽  
Frequency Estimation ◽  
Estimation Algorithm ◽  
Linear Arrays ◽  
Propagator Method ◽  
Joint Direction ◽  
Parallel Factor ◽  
Esprit Algorithm ◽  
Spatial Sparsity ◽  
Better Than

In this paper, a joint direction of arrival (DOA) and frequency estimation algorithm of narrow-band signals is proposed via compressed sensing (CS) parallel factor (PARAFAC) framework. The proposed algorithm constructs the data model into a PARAFAC model, and compresses it to a smaller one. Then trilinear alternating least-squares (TALS) algorithm is exploited to estimate the compressed parameter matrices, and finally the joint DOA and frequency estimation is obtained via the spatial sparsity and the frequency sparsity. Due to compression, the proposed algorithm has lower computational complexity than the conventional PARAFAC algorithm, and saves more memory capacity for practical application. The DOA and frequency estimation performance of the proposed algorithm is very close to that of the conventional PARAFAC algorithm, and better than those of the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm and the propagator method (PM). Furthermore, the proposed algorithm can achieve automatically paired DOA and frequency estimation. Besides, it is applicable for nonuniform linear arrays. Effectiveness of the proposed algorithm is assessed by simulations.

scholarly journals Joint 2D-DOA and Frequency Estimation for L-Shaped Array Using Iterative Least Squares Method

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Ling-yun Xu ◽  
Xiao-fei Zhang ◽  
Zong-ze Xu ◽  
Miao Yu
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Frequency Estimation ◽  
Spectral Peak ◽  
Propagator Method ◽  
Esprit Algorithm ◽  
Delay Matrix

We introduce an iterative least squares method (ILS) for estimating the 2D-DOA and frequency based on L-shaped array. The ILS iteratively finds direction matrix and delay matrix, then 2D-DOA and frequency can be obtained by the least squares method. Without spectral peak searching and pairing, this algorithm works well and pairs the parameters automatically. Moreover, our algorithm has better performance than conventional ESPRIT algorithm and propagator method. The useful behavior of the proposed algorithm is verified by simulations.

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