scholarly journals Open-Lake Experimental Investigation of Azimuth Angle Estimation Using a Single Acoustic Vector Sensor

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Anbang Zhao ◽  
Lin Ma ◽  
Juan Hui ◽  
Caigao Zeng ◽  
Xuejie Bi
Keyword(s):  
Acoustic Pressure ◽  
Azimuth Angle ◽  
Estimation Methods ◽  
Acoustic Vector Sensor ◽  
Angle Estimation ◽  
Passive Detection ◽  
Vector Sensor ◽  
Sonar System ◽  
Multichannel Signals ◽  
Structure Sensor

Five well-known azimuth angle estimation methods using a single acoustic vector sensor (AVS) are investigated in open-lake experiments. A single AVS can measure both the acoustic pressure and acoustic particle velocity at a signal point in space and output multichannel signals. The azimuth angle of one source can be estimated by using a single AVS in a passive sonar system. Open-lake experiments are carried out to evaluate how these different techniques perform in estimating azimuth angle of a source. The AVS that was applied in these open-lake experiments is a two-dimensional accelerometer structure sensor. It consists of two identical uniaxial velocity sensors in orthogonal orientations, plus a pressure sensor—all in spatial collocation. These experimental results indicate that all these methods can effectively realize the azimuth angle estimation using only one AVS. The results presented in this paper reveal that AVS can be applied in a wider range of application in distributed underwater acoustic systems for passive detection, localization, classification, and so on.

scholarly journals Partial Angular Sparse Representation Based DOA Estimation Using Sparse Separate Nested Acoustic Vector Sensor Array

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4465 ◽  
Author(s):  
Jianfeng Li ◽  
Zheng Li ◽  
Xiaofei Zhang
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Sensor Array ◽  
Doa Estimation ◽  
Sparse Recovery ◽  
Estimation Methods ◽  
Acoustic Vector Sensor ◽  
Angle Estimation ◽  
Vector Sensor ◽  
Cross Covariance

In this paper, the issue of direction of arrival (DOA) estimation is discussed, and a partial angular sparse representation (SR)-based method using a sparse separate nested acoustic vector sensor (SSN-AVS) array is developed. Traditional AVS array is improved by separating the pressure sensor array and velocity sensor array into two different sparse array geometries with nested relationship. This improved array geometry can achieve large degrees of freedom (DOF) after the extended vectorization of the cross-covariance matrix, and only partial SR of the angle is required by exploiting the cyclic phase ambiguity caused by the large inter-element spacing of the virtual array. Joint sparse recovery is developed to amend the grid offset and unitary transformation is utilized to transform the complex atoms into real-valued ones. After sparse recovery, the sparse vector can simultaneously provide high-resolution but ambiguous angle estimation and unambiguous reference angle estimation embedded in the AVS array, and they are combined to obtain unique and high-resolution DOA estimation. Compared to other state-of-the-art DOA estimation methods using the AVS array, the proposed algorithm can provide better DOA estimation performance while requiring lower complexity. Multiple simulation results verify the effectiveness of the approach.

scholarly journals A Novel Self-Calibration Method for Acoustic Vector Sensor

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Yao Zhang ◽  
Jin Fu ◽  
Guannan Li
Keyword(s):  
Acoustic Pressure ◽  
Calibration Method ◽  
Estimation Accuracy ◽  
Spatial Gradient ◽  
Arrival Angle ◽  
Acoustic Vector Sensor ◽  
Self Calibration ◽  
Vector Sensor ◽  
Calibration Algorithm ◽  
Simulation Results

The acoustic vector sensor (AVS) can measure the acoustic pressure field’s spatial gradient, so it has directionality. But its channels may have nonideal gain/phase responses, which will severely degrade its performance in finding source direction. To solve this problem, in this study, a self-calibration algorithm based on all-phase FFT spectrum analysis is proposed. This method is “self-calibrated” because prior knowledge of the training signal’s arrival angle is not required. By measuring signals from different directions, the initial phase can be achieved by taking the all-phase FFT transform to each channel. We use the amplitude of the main spectrum peak of every channel in different direction to formulate an equation; the amplitude gain estimates can be achieved by solving this equation. In order to get better estimation accuracy, bearing difference of different training signals should be larger than a threshold, which is related to SNR. Finally, the reference signal’s direction of arrival can be estimated. This method is easy to implement and has advantage in accuracy and antinoise. The efficacy of this proposed scheme is verified with simulation results.

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