Near-field sound source localization using principal component analysis–multi-output support vector regression

Lanmei Wang,Yao Wang,Jianke Jia,Guibao Wang

doi:10.1177/1550147720916405

Abstract

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

Highlights

The estimation of direction of arrival (DOA) has been widely used in many research fields, such as passive location, sonar array direction finding, seismic and geological resource detection, and mobile communication
The traditional spatial spectrum estimation algorithm usually assumes that the signal source is located in the far field of the array, that is, the range from the source to the array is far enough, so that the spherical wavefront of the signal radiation can be approximated as a plane wavefront at the receiving array
Starer and Nehorai[11] proposed an improved multiple signal classification (MUSIC) algorithm based on the path tracking method, the 2-dimensional (2-D) search problem of near-field MUSIC algorithm was converted into the 1dimensional (1-D) search problem, and the 2-D parameters of the source were estimated by the iterative method

Summary

Introduction

The estimation of direction of arrival (DOA) has been widely used in many research fields, such as passive location, sonar array direction finding, seismic and geological resource detection, and mobile communication. The near-field source signal received by linear array is a 2-D parameter of distance and angle, so the calculation of direct parameter estimation is too large. Most of the near-field source subspace-based parameter estimation method needs to realize the decoupling of distance and angle under specific array arrangement and certain approximate conditions. When multiple near-field source signals are incident to the receiving array at the same time, SVM regression becomes the regression of multiple 2-D parameters, and the algorithm becomes more complex. PCA method is introduced into the SVM regression algorithm of near-field parameter estimation, and PCA is used to extract the number of input features of SVM regression algorithm and reduce noise. In most of the literature, the upper triangular element of the covariance matrix of the received signal is used as the input to the SVR machine. In order to optimize equation (7), the iteratively reweighted least squares (IRWLS) procedure is needed

X N wj2

Initialization

Findings

Conclusion