A four-element optical fiber 4C vector hydrophone array

2012 ◽  
Author(s):  
Jianfei Wang ◽  
Hong Luo ◽  
Yu Chen ◽  
Zhou Meng
Keyword(s):  
Optical Fiber ◽  
Vector Hydrophone ◽  
Hydrophone Array

Pressure Optical Fiber Vector Hydrophone Made of Thin-Walled Cylindrical Shell

2008 ◽  
Vol 35 (8) ◽  
pp. 1214-1219 ◽  
Author(s):  
康崇 Kang Chong ◽  
张敏 Zhang Min ◽  
陈洪娟 Chen Hongjuan ◽  
庞盟 Pang Meng ◽  
吕文磊 Lü Wenlei ◽  
...  
Keyword(s):  
Cylindrical Shell ◽  
Optical Fiber ◽  
Vector Hydrophone ◽  
Thin Walled

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

scholarly journals Vector Hydrophone Array Design Based on Off-Grid Compressed Sensing

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6949
Author(s):  
Zhibo Shi ◽  
Guolong Liang ◽  
Longhao Qiu ◽  
Guangming Wan ◽  
Lei Zhao
Keyword(s):  
Compressed Sensing ◽  
Array Signal Processing ◽  
Simultaneous Optimization ◽  
L1 Norm ◽  
Array Design ◽  
Vector Hydrophone ◽  
Grid Algorithm ◽  
The Difference ◽  
Acoustic Environments ◽  
Hydrophone Array

Array design is the primary consideration for array signal processing, and sparse array design is an important and challenging task. In underwater acoustic environments, the vector hydrophone array contains more information than the scalar hydrophone array, but there are few articles focused on the design of the vector hydrophone array. The difference between the vector hydrophone array and the scalar hydrophone array is that each vector hydrophone has three or four channels. When designing a sparse vector hydrophone array, these channels need to be optimized at the same time to ensure the sparsity of the array elements’ number. To solve this problem, this paper introduced the compressed sensing (CS) theory into the vector hydrophone array design, constructed the vector hydrophone array design problem into a globally solvable optimization problem, proposed a CS-based algorithm with the L1 norm suitable for vector hydrophone array, and realized the simultaneous optimization of multiple channels from the same vector hydrophone. At the same time, the off-grid algorithm was added to obtain higher design accuracy. Two design examples verify the effectiveness of the proposed method. The theoretical analysis and simulation results show that compared with the conventional compressed sensing algorithm with the same aperture, the algorithm proposed in this paper used fewer vector hydrophone elements to obtain better fitting of the desired beam pattern.

Bearing estimation for optical fiber vector hydrophone with in-band resonance

2020 ◽  
Vol 158 ◽  
pp. 107055
Author(s):  
Chengyan Peng ◽  
Xueliang Zhang ◽  
Zhou Meng
Keyword(s):  
Optical Fiber ◽  
Vector Hydrophone ◽  
Bearing Estimation

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

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.

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