This paper presents the ESPRIT algorithm based on a high-order cumulant for direction of arrival(DOA) estimation using a single vector hydrophone that overcomes limitations of the autocorrelation algorithm.This method forms a temporal invariance via two time-delayed data sets collected from one vector hydrophone and constructs two sub-arrays with the same array shape.The ESPRIT algorithm based on a high-order cumulant was formulated by applying the orthogonality of high-resolution signal subspace and noise subspace and signal subspace rotation invariance to the cumulant domain.Simulation results show that the influence of white Gaussian noise is fully suppressed with the aid of the high-order cumulant and the method can provide high-precision,high-resolution asymptotically unbiased bearing estimates at an SNR of 6dB in Gaussian colored noise.

ESPRIT algorithm for DOA estimation using a single vector hydrophone

A method of parameter matching is proposed for the ESPRIT algorithm,which is used to estimate 2-D direction-of-arrival(DOA).This simplifies the complexity of the algorithm and improves the parameter matching rate.Parameter matching is automatically achieved by only one eigenvalue decomposition for one of the two matrixes containing the information of DOA signals.It is based on the relationships of the matrix,its eigenvalues,and corresponding eigenvectors, and this process is followed by a simple division operation.No repetitive matrix transforms are required.This method can simplify the process of parameter matching without affecting the parameter performance of a 2-D ESPRIT algorithm.A computer simulation verified the effectiveness of the method.

Fast parameter matching for 2-D ESPRIT algorithm

The eigenvalues estimation and association of subspace rotation invariance is an improtant problem in the multidimensional spectral and array processing.This paper exploits multiple invariance subspace of the second-order statistic of the observations collected from parallel crossed dipoles uniform linear array,and automatically paired frequency,two-dimensional directions and the polarization of incoming narrow-band signals via single signal subspace as long as each signal parameters are unique.The algorithm provides multiple parameter estimation via single eigenvector matrix that neither requires any expensive search procedure nor any other pairing strategy.Moreover it does not break down if several independent signals have common parameters.The Monte Carlo simulations are shown to be in close agreement with theoretically analysis.

Multiple parameters joint estimation via single signal subspace

A vector-hydrophone simultaneously measures acoustic pressure and three orthogonal components of acoustic particle velocity at a particular point in a sound field.A single vector hydrophone is a spatial collocated array.In this paper,a data model for an arbitrary vector hydrophone array was established and a non-spatial ESPRIT algorithm based on this arbitrary vector hydrophone was studied.The matrix pair of the non-spatial ESPRIT algorithm was composed of different output components of the vector hydrophone,instead of the two identical subarrays,allowing it to estimate the direction of arrival of targets.2-D direction finding(azimuth and elevation angle) could be obtained using a non-spatial ESPRIT algorithm.Direction of arrival estimations for single source and multisouces were simulated.The efficiency of the proposed algorithm was tested with experimental data.The RMS error for angle estimation by the non-spatial ESPRIT algorithm was about 3° and the main beam width of conventional beamforming was about 30°,which indicates that high resolution direction of arrival estimation could be obtained using the non-spatial ESPRIT algorithm without requiring information from an array manifold.

A non-spatial ESPRIT algorithm for a vector hydrophone array

Purpose This paper aims to address the challenges in hydroacoustic signal detection, signal distortion and target localization caused by baseline drift. The authors propose a combined algorithm that integrates short-time Fourier transform (STFT) detection, smoothness priors approach (SPA), attitude calibration and direction of arrival (DOA) estimation for micro-electro-mechanical system vector hydrophones. Design/methodology/approach Initially, STFT method screens target signals with baseline drift in low signal-to-noise ratio environments, facilitating easier subsequent processing. Next, SPA is applied to the screened target signal, effectively removing the baseline drift, and combined with filtering to improve the signal-to-noise ratio. Then, vector channel amplitudes are corrected using attitude correction with 2D compass data. Finally, the absolute target azimuth is estimated using the minimum variance distortion-free response beamformer. Findings Simulation and experimental results demonstrate that the SPA outperforms high-pass filtering in removing baseline drift and is comparable to the effectiveness of variational mode decomposition, with significantly shorter processing times, making it more suitable for real-time applications. The detection performance of the STFT method is superior to instantaneous correlation detection and sample entropy methods. The final DOA estimation achieves an accuracy within 2°, enabling precise target azimuth estimation. Originality/value To the best of the authors’ knowledge, this study is the first to apply SPA to baseline drift removal in hydroacoustic signals, significantly enhancing the efficiency and accuracy of signal processing. It demonstrates the method’s outstanding performance in the field of underwater signal processing. In addition, it confirms the reliability and feasibility of STFT for signal detection in the presence of baseline drift. 

Research on underwater target signal orientation estimation based on smoothness priors approach

Based on a single vector hydrophone, two sub-matrix of the same formation which formed signal data matrix was constructed by delayed time data. On the basis of LS-ESPRIT, the signal data matrix was decomposed by one eigenvalue decomposition to get its eigenvalues and corresponding eigenvectors, which included the information of frequency and angle, so two-dimensional DOA and frequency joint estimation was realized for narrow-band signal. This method had better estimation accuracy, which didn't need any search procedure, the three-dimensional parameter matching was automatically achieved by this method. Compare with DOA matrix method via a single vector hydrophone and LS-ESPRIT method via acoustic pressure array by MATLAB, the results showed that this method have better estimation performance.

Estimation of 2-D DOA joint frequency of signal via a single vector hydrophone

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Estimation of 2-D DOA joint frequency of signal via a single vector hydrophone