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.

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 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 ◽  
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).

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.

scholarly journals Multi-Frequency Based Direction-of-Arrival Estimation for 2q-Level Nested Radar & Sonar Arrays

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3385 ◽  
Author(s):  
Hao Zhou ◽  
Guoping Hu ◽  
Junpeng Shi ◽  
Ziang Feng
Keyword(s):  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Research Area ◽  
Frequency Separation ◽  
Frequency Method ◽  
Virtual Sensors ◽  
Order Difference ◽  
Sonar Systems ◽  
Minimum Number

Direction finding is a hot research area in radar and sonar systems. In the case of q ≥ 2, the 2qth-order cumulant based direction of arrival (DOA) estimation algorithm for the 2q-level nested array can achieve high resolution performance. A virtual 2qth-order difference co-array, which contains O(N2q) virtual sensors in the form of a uniform linear array (ULA), is yielded and the Gaussian noise is eliminated. However, some virtual elements are separated by the holes among the 2qth-order difference co-array and cannot be fully used. Even though the application of the multi-frequency method for minimum frequency separation (MFMFS) can fill the holes with low computation complexity, it requires that the number of frequencies must increase with the number of holes. In addition, the signal spectra have to be proportional for all frequencies, which is hard to satisfy when the number of holes is large. Aiming at this, we further propose a multi-frequency method for a minimum number of frequencies (MFMNF) and discuss the best frequency choice under two specific situations. Simulation results verify that, compared with the MFMFS method, the proposed MFMNF method can use only one frequency to fill all the holes while achieving a longer virtual array and the DOA estimation performance is, therefore, improved.

scholarly journals Minimum Array Elements for Resolution of Several Direction of Arrival Estimation Methods in Various Noise-Level Environments

2018 ◽  
Vol 2 ◽  
pp. 87-94
Author(s):  
Ismail El Ouargui ◽  
Said Safi ◽  
Miloud Frikel
Keyword(s):  
Gaussian Noise ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Minimum Number ◽  
Informative Signals

The resolution of a Direction of Arrival (DOA) estimation algorithm is determined based on its capability to resolve two closely spaced signals. In this paper, authors present and discuss the minimum number of array elements needed for the resolution of nearby sources in several DOA estimation methods. In the real world, the informative signals are corrupted by Additive White Gaussian Noise (AWGN). Thus, a higher signal-to-noise ratio (SNR) offers a better resolution. Therefore, we show the performance of each method by applying the algorithms in different noise level environments.

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 Novel Diagonal Reloading Based Direction of Arrival Estimation in Unknown Non-Uniform Noise

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Hao Zhou ◽  
Guoping Hu ◽  
Junpeng Shi ◽  
Ziang Feng
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Main Diagonal ◽  
Sample Covariance ◽  
Uniform Noise ◽  
Negative Effect ◽  
Signal Covariance Matrix

Nested array can expand the degrees of freedom (DOF) from difference coarray perspective, but suffering from the performance degradation of direction of arrival (DOA) estimation in unknown non-uniform noise. In this paper, a novel diagonal reloading (DR) based DOA estimation algorithm is proposed using a recently developed nested MIMO array. The elements in the main diagonal of the sample covariance matrix are eliminated; next the smallest MN-K eigenvalues of the revised matrix are obtained and averaged to estimate the sum value of the signal power. Further the estimated sum value is filled into the main diagonal of the revised matrix for estimating the signal covariance matrix. In this case, the negative effect of noise is eliminated without losing the useful information of the signal matrix. Besides, the degrees of freedom are expanded obviously, resulting in the performance improvement. Several simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

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