Efficient DOA Estimation Method with Ambient Noise Elimination for Array of Underwater Acoustic Vector Sensors

2021 ◽  
Author(s):  
Aifei Liu ◽  
Shengguo Shi ◽  
Xinyi Wang
Keyword(s):  
Ambient Noise ◽  
Estimation Method ◽  
Doa Estimation ◽  
Noise Elimination ◽  
Underwater Acoustic ◽  
Vector Sensors

scholarly journals Tensor Approach to DOA Estimation of Coherent Signals with Electromagnetic Vector-Sensor Array

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4320 ◽  
Author(s):  
Ming-Yang Cao ◽  
Xingpeng Mao ◽  
Xiaozhuan Long ◽  
Lei Huang
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Parameters Estimation ◽  
Estimation Accuracy ◽  
Coherent Signals ◽  
Rectangular Array ◽  
Vector Sensor ◽  
Electromagnetic Vector Sensor ◽  
Vector Sensors ◽  
Uniform Rectangular Array

This paper addresses the direction-of-arrival (DOA) estimation problem using a uniform rectangular array with electromagnetic vector-sensors in correlated/coherent signal environments. The polarization information is separated from the steering matrix to decorrelate the signals. By developing a tensorial structured received measurements of the array, we propose a tensor-based eigenvector DOA estimation method. Then we apply the forward-backward averaging to the tensor since it obeys the centro-Hermitian structure. In addition, a tensor-based polarization parameters estimation method is presented. The proposed algorithms are superior to the state-of-the-art algorithms in terms of estimation accuracy of coherent signals while only demand a modest computation burden comparing with the latter ones. Simulation results are given to demonstrate the effectiveness of the proposed methods under different scenarios.

scholarly journals Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays

2021 ◽  
Vol 9 (2) ◽  
pp. 127
Author(s):  
Guolong Liang ◽  
Zhibo Shi ◽  
Longhao Qiu ◽  
Sibo Sun ◽  
Tian Lan
Keyword(s):  
Ambient Noise ◽  
Bayesian Learning ◽  
Colored Noise ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Noise Model ◽  
Superior Performance ◽  
Sparse Bayesian Learning ◽  
Vector Hydrophone

Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored, which may severely degrade the performance of DOA estimation. To solve this problem, this paper proposes a DOA estimation method based on sparse Bayesian learning with the modified noise model using acoustic vector hydrophone arrays. Firstly, an applicable linear noise model is established by using the prolate spheroidal wave functions (PSWFs) to characterize spatially colored noise and exploiting the excellent performance of the PSWFs in extrapolating band-limited signals to the space domain. Then, using the proposed noise model, an iterative method for sparse spectrum reconstruction is developed under a sparse Bayesian learning (SBL) framework to fit the actual noise field received by the acoustic vector hydrophone array. Finally, a DOA estimation algorithm under the modified noise model is also presented, which has a superior performance under spatially colored noise. Numerical results validate the effectiveness of the proposed method.

scholarly journals In-Depth Exploration of Signal Self-Cancellation Phenomenon to Achieve DOA Estimation of Underwater Acoustic Sources

2019 ◽  
Vol 9 (3) ◽  
pp. 570 ◽  
Author(s):  
Fang Wang ◽  
Yong Chen ◽  
Jianwei Wan
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Minimum Variance ◽  
Estimation Accuracy ◽  
Underwater Acoustic ◽  
Direction Estimation ◽  
Minimum Variance Distortionless Response ◽  
Acoustic Sources ◽  
Ocean Environment ◽  
Spatial Power Spectrum

In the ocean environment, the minimum variance distortionless response beamformer usually has the problem of signal self-cancellation, that is, the acoustic signal of interest is erroneously suppressed as interference. By exploring the useful information behind the signal self-cancellation phenomenon, a high-precision direction estimation method for underwater acoustic sources is proposed. First, a pseudo spatial power spectrum is obtained by performing unit circle mapping on the beam response in the direction interval. Second, the online calculation process is given for reducing the computational complexity. The computer simulation results show that the proposed algorithm can obtain satisfactory direction estimation accuracy under the conditions of low intensity of acoustic source, strong interference and noise, and less array snapshot data.

scholarly journals An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4403
Author(s):  
Ji Woong Paik ◽  
Joon-Ho Lee ◽  
Wooyoung Hong
Keyword(s):  
Covariance Matrix ◽  
Phased Array ◽  
Null Space ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Improve Performance ◽  
Space Projection

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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