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.

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.

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

scholarly journals Underdetermined DOA Estimation of Wideband LFM Signals Based on Gridless Sparse Reconstruction in the FRF Domain

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2383 ◽  
Author(s):  
Yue Cui ◽  
Junfeng Wang ◽  
Jie Qi ◽  
Zhanying Zhang ◽  
Jinqi Zhu
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Sparse Matrix ◽  
Estimation Method ◽  
Doa Estimation ◽  
Sparse Reconstruction ◽  
Virtual Sensors ◽  
Steering Vector ◽  
Wideband Signals ◽  
Variant Frequency

An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant steering vector is firstly derived based on a coprime array. Afterwards, by interpolating virtual sensors, a virtual extended uniform linear array (ULA) is constructed with more degrees of freedom, and its covariance matrix in the FRF domain is recovered by employing sparse matrix reconstruction. Meanwhile, in order to avoid the grid mismatch problem, the modified atomic norm minimization is used to retrieve the covariance matrix with the consecutive basis. Different from the existing methods that approximately assume the frequency and the steering vector of the wideband signals are time-invariant in every narrowband frequency bin, the proposed method not only can directly solve more DOAs of LFM signals than the number of physical sensors with time-variant frequency and steering vector, but also obtain higher resolution and more accurate DOA estimation performance by the gridless sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed method.

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.

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 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.

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.

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