DOA estimation method of weak sources for an array antenna under strong interference conditions

2018 ◽  
Vol 105 (11) ◽  
pp. 1931-1944
Author(s):  
Jian Gong ◽  
Shuntian Lou ◽  
Yiduo Guo
2012 ◽  
Vol 195-196 ◽  
pp. 109-114
Author(s):  
Yue Cui ◽  
Kai Hua Liu ◽  
Jun Feng Wang

A new direction of arrival (DOA) estimation method is proposed for coherent GPS signals, which applies to the GPS receiver with rich multipath, strong interference and low SNR. In this method, the interference is first suppressed by projecting the received signal onto the interference orthogonal subspace. Afterwards, the noise is eliminated by the spatial difference matrix. Then, the coherent GPS signals are decorrelated by the reconstructed Toeplitz matrix, and the DOA estimation is improved by a new constructed matrix. The proposed method can obtain more accurate DOA estimation for coherent GPS signals with strong interference and low SNR in the GPS receiver. Simulation results demonstrate the effectiveness and efficiency of the proposed method.


Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4403
Author(s):  
Ji Woong Paik ◽  
Joon-Ho Lee ◽  
Wooyoung Hong

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.


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