Quaternion ESPRIT parameter estimation method of sparse circular sound vector sensor array

Abstract

Provided is a quaternion ESPRIT parameter estimation method of a sparse circular sound vector sensor array. The array receives K far-field narrowband sound signals of different frequencies and independent from one another, according to each of two groups of complex-domain data received by the array, the same sound pressure of sampling data of the array is overlapped with vibration speed data in three directions vertical to one another to form quaternion linear sampling data; characteristic decomposition and related operation are carried out on an autocorrelation matrix of full-array reception data to obtain estimated values of array guiding vectors; the estimated values of the array guiding vectors are used to reconstruct subarray guiding vectors, and the rotation-invariant relation among the subarray guiding vectors is used to obtain a roughly estimated value of the cosine in the signal direction; and the roughly estimated value of the cosine in the signal direction is used to determine a phase period fuzzy number of a spatial domain guiding vector, and a least square method is used to obtain an accurate estimated value of the arrival angle of the signal. According to the invention, orthogonal feature among components of the sound vector sensors is maintained, error robustness of a model is higher, and the problem of phase fuzziness of the sparse vector sensor circular array is solved.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a quaternion ESPRIT arrival angle estimation method of an acoustic vector sensor array. Background technique [0002] The acoustic vector sensor is a new type of sound source signal direction finding equipment. The acoustic vector sensor technology has attracted the attention of many scholars at home and abroad in recent years. The acoustic vector sensor consists of a traditional non-directional sound pressure sensor and a particle with dipole directivity. Composed of vibration velocity sensors, the acoustic vector sensor can measure the sound pressure intensity and particle vibration velocity of a certain point in the sound field synchronously and at the same point. Since the dipole directivity of the acoustic vector sensor has nothing to do with frequency, it is more suitable for low-frequency signals measurement and testing; [0003] Acoustic vector sens...

AI Technical Summary

Problems solved by technology

The long vector model simply arranges the output components of the acoustic vector sensor into a long vector without considering the orthogonal relationship between the output components of the acoustic vector sensor. Therefore, the long vector model cannot fully reflect the superiority of the acoustic vector sensor technology

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