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

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.

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Blind DOA Estimation Based on Single Acoustic Vector Hydrophone

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.706-708.678 ◽

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.

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

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20203810014 ◽

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.

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Vector Hydrophone Array Development and its Associated DOA Estimation Algorithms

OCEANS 2006 - Asia Pacific ◽

10.1109/oceansap.2006.4393852 ◽

2006 ◽

Cited By ~ 9

Author(s):

Nan Zou ◽

Chia Chin Swee ◽

Bertha A. L. Chew

Keyword(s):

Doa Estimation ◽

Estimation Algorithms ◽

Vector Hydrophone ◽

Hydrophone Array

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Biquaternion Model of Vector Hydrophone Array and DOA Estimation Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.92 ◽

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.

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Study on Vector Hydrophone Array DOA Estimation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.2589 ◽

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.

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A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensors ◽

10.3390/s21062191 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2191

Author(s):

Huichao Yan ◽

Ting Chen ◽

Peng Wang ◽

Linmei Zhang ◽

Rong Cheng ◽

...

Keyword(s):

Compressed Sensing ◽

Spatial Clustering ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Cluster Center ◽

Direction Of Arrival Estimation ◽

Vector Hydrophone ◽

Regularization Parameters ◽

Selection Of

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

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