DOA Estimation of Surface Vessel under Correlated Noises Using Vector Hydrophone Array

2012 ◽  
Vol 542-543 ◽  
pp. 1362-1365
Author(s):  
Guang Jin He ◽  
Jin Fang Cheng ◽  
Jie Xu
Keyword(s):  
Doa Estimation ◽  
Vector Data ◽  
Music Algorithm ◽  
Complex Vector ◽  
Complex Quaternion ◽  
Vector Hydrophone ◽  
Multiple Frames ◽  
Hydrophone Array ◽  
Surface Vessel ◽  
Correlated Noises

In the traditional acoustic vector data processing, the output of a single vector hydrophone is modeled as a complex vector, which cannot hold the orthogonal structure of the hydrophone. Here, the complex-quaternion model of the vector hydrophone is proposed. The velocity elements are put in the position of the three imaginary parts, which retains the orthogonality of the velocity sensors. As the non-stationary properties of the surface vessel’s radiated signals, the received data are divided into multiple frames. The covariance matrices and their vectorizations of each frame are calculated. An orthogonal projection is employed to eliminate the background noises. Then the noise-free covariance matrix is used to estimate the DOA’s of the sources by taking use of MUSIC algorithm. The simulations verify the good performance of the proposed algorithm.

Biquaternion Model of Vector Hydrophone Array and DOA Estimation Algorithm

2012 ◽  
Vol 239-240 ◽  
pp. 92-95
Author(s):  
Guang Jin He ◽  
Jin Fang Cheng ◽  
Wei Zhang
Keyword(s):  
Data Model ◽  
Traditional Approach ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Vector Data ◽  
Complex Vector ◽  
Vector Hydrophone ◽  
Particle Velocities ◽  
Hydrophone Array

In the traditional vector data expressions, the outputs of a single 3D vector hydrophone are reorganized into a complex vector, which cannot retain the orthogonality of the velocity elements. In this paper, biquaternion formalism is used to model the vector hydrophone’s output and a novel MUSIC-like algorithm is proposed to estimate the DOA (Direction-Of-Arrival) of the sources. The three velocity channels outputs are placed in the imaginary parts of the biquaternion numbers, which retains the orthogonality of the particle velocities and is robust to correlated/coherent noises. What’s more, the biquaternion data model has a compact way of handing multi-component data, which results a much less memory requirements compared with the traditional approach.

Study on Vector Hydrophone Array DOA Estimation

2014 ◽  
Vol 543-547 ◽  
pp. 2589-2593
Author(s):  
Hao Zhou ◽  
Wen Lin Huang
Keyword(s):  
Particle Velocity ◽  
Acoustic Pressure ◽  
Model Simulation ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Music Algorithm ◽  
Subspace Decomposition ◽  
Vector Hydrophone ◽  
Orthogonal Components ◽  
Hydrophone Array

Vector hydrophone is composed of acoustic pressure sensor and particle velocity sensor, which can simultaneously measure acoustic pressure and orthogonal components of particle velocity. MUSIC (Multiple Signal Classification) algorithm is a high resolution spatial spectrum analysis method based on subspace decomposition. This paper introduces the operation principles of this algorithm in detail and investigates the application of MUSIC algorithm to the DOA estimation of acoustic sources by a vector-hydrophone ULA (Uniform Linear Array) output model. Simulation results indicate that the resolution capability of MUSIC algorithm under larger SNRs is excellent.

USTC-MUSIC Algorithm for DOA Estimation of Coherent Sources Using Single Vector Hydrophone

2014 ◽  
Vol 530-531 ◽  
pp. 530-533
Author(s):  
Jin Fang Cheng ◽  
Chao Ran Zhang ◽  
Wei Zhang
Keyword(s):  
Correlation Matrix ◽  
Temporal Correlation ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Music Algorithm ◽  
Correlation Matrices ◽  
Vector Hydrophone ◽  
Coherent Sources ◽  
Simulation Results ◽  
Spatio Temporal

The MUSIC algorithm cannot deal with the problem of DOA estimation of coherent sources, this paper proposes the USTC (unitary spatio-temporal correlation matrices)-MUSIC algorithm using single vector hydrophone to solve this problem, by utilizing the unitary spatio-temporal correlation matrix instead of the covariance matrix. The simulation results demonstrate that the USTC-MUSIC algorithm has a better ability to distinguish the coherent sources from different directions than the spatial smoothing MUSIC algorithm.

scholarly journals An Improved Squirrel Search Algorithm for Maximum Likelihood DOA Estimation and Application for MEMS Vector Hydrophone Array

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 118343-118358 ◽  
Author(s):  
Peng Wang ◽  
Yujun Kong ◽  
Xuefang He ◽  
Mingxing Zhang ◽  
Xiuhui Tan
Keyword(s):  
Maximum Likelihood ◽  
Search Algorithm ◽  
Doa Estimation ◽  
Vector Hydrophone ◽  
Hydrophone Array

Off-Grid DOA Estimation Based on Alternating Iterative Weighted Least Squares for Acoustic Vector Hydrophone Array

2020 ◽  
Vol 39 (9) ◽  
pp. 4650-4680 ◽  
Author(s):  
Weidong Wang ◽  
Qunfei Zhang ◽  
Wentao Shi ◽  
Weijie Tan ◽  
Linlin Mao
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Doa Estimation ◽  
Vector Hydrophone ◽  
Hydrophone Array

Blind DOA Estimation Based on Single Acoustic Vector Hydrophone

2013 ◽  
Vol 706-708 ◽  
pp. 678-681
Author(s):  
Da Wei Xiao ◽  
Jin Fang Cheng ◽  
Jing Zhuo Zhang
Keyword(s):  
Particle Velocity ◽  
Acoustic Waves ◽  
Simulation Experiment ◽  
Estimation Method ◽  
Doa Estimation ◽  
New Method ◽  
Steering Vector ◽  
Vector Hydrophone ◽  
Hydrophone Array ◽  
Signal Vector

In this paper a blind direction of arrival (DOA) estimation method based on single acoustic vector hydrophone is presented according to the characteristics of underwater acoustic waves. With no priori assumptions about the structure of steering vector, the vector hydrophone array manifold is estimated by the joint approximate diagonalization of eigen-matrices (JADE) algorithm. Simultaneously, acoustic waves are reconstructed by the separation matrix and signal vector. Finally, with the orthogonality of the components of particle velocity the DOA are estimated. Simulation experiment proves the effectiveness and correctness of this new method.

scholarly journals Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array

2020 ◽  
Vol 38 (1) ◽  
pp. 14-23
Author(s):  
Weidong Wand ◽  
Qunfei Zhang ◽  
Wentao Shi ◽  
Juan SHI ◽  
Weijie Tan ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Doa Estimation ◽  
Sparse Signal ◽  
Average Error ◽  
Vector Hydrophone ◽  
Matrix Fitting ◽  
Sparse Signal Representation ◽  
Representation Method ◽  
Hydrophone Array ◽  
Present Algorithm

Aiming at the direction of arrival (DOA) estimation of coherent signals in vector hydrophone array, an iterative sparse covariance matrix fitting algorithm is proposed. Based on the fitting criterion of weighted covariance matrix, the objective function of sparse signal power is constructed, and the recursive formula of sparse signal power iteration updating is deduced by using the properties of Frobenius norm. The present algorithm uses the idea of iterative reconstruction to calculate the power of signals on discrete grids, so that the estimated power is more accurate, and thus more accurate DOA estimation can be obtained. The theoretical analysis shows that the power of the signal at the grid point solved by the present algorithm is preprocessed by a filter, which allows signals in specified directions to pass through and attenuate signals in other directions, and has low sensitivity to the correlation of signals. The simulation results show that the average error estimated by the present method is 39.4% of the multi-signal classification high resolution method and 73.7% of the iterative adaptive sparse signal representation method when the signal-to-noise ratio is 15 dB and the non-coherent signal. Moreover, the average error estimated by the present method is 12.9% of the iterative adaptive sparse signal representation method in the case of coherent signal. Therefore, the present algorithm effectively improves the accuracy of target DOA estimation when applying to DOA estimation with highly correlated targets.

scholarly journals A Novel Extended Cyclic MUSIC algorithm using Wavelet Decomposition Technique

2020 ◽  
Vol 6 (8) ◽  
pp. 24-27
Keyword(s):  
Communication Systems ◽  
Wavelet Decomposition ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Wireless Communication Systems ◽  
Music Algorithm ◽  
Data Array ◽  
Spectral Correlation ◽  
Spatially Correlated ◽  
Correlated Noises

The study and analysis of direction of arrival(DOA) estimation has been a very challenging aspect in wireless communication systems, radar and sonar in the presence of a fading channel. Extended cyclic MUSIC subspace based method is reviewed for the DOA when noise is present. An extension of the Cyclic MUSIC algorithm is the Extended mode of the cyclic MUSIC which uses an extended version of the data array vector. Based upon the spectral correlation of the inbound signals these algorithms can estimate the signals’ direction of arrival. The spatially correlated noises, present degrade the performance of the Cyclic MUSIC algorithms and also fail to make an estimate of the signals' direction of arrival when they are closely spaced. The paper proposes an improved DOA estimation technique of Wavelet Decomposition when spatially correlated noises are present. The signals present at the receiver side are denoised and the required estimates of the transmitted signals are obtained by using the Extended Cyclic MUSIC algorithm to the denoised data. Simulations prove that the output SNR has been enhanced considerably.

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