Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Acoustic vector circular array source number detection method based on characteristic value multiple threshold correction

A detection method and eigenvalue technology, which are applied to direction finders using ultrasonic/sonic/infrasonic waves, etc., can solve the problem that the combined processing method of sound pressure, vibration and velocity of a circular array of sound vectors cannot be applied, and can reduce the threshold of signal-to-noise ratio, The effect of good detection performance

Active Publication Date: 2016-09-21
HARBIN ENG UNIV
View PDF4 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The present invention proposes an acoustic vector circular array signal source number detection method based on eigenvalue multi-threshold correction, which overcomes the traditional minimum description length criterion (MDL), diagonally loaded MDL, Geiger's circle detection criterion (GDE), etc. The detection method is sensitive to the change of the noise characteristic value, but cannot apply the joint processing method of sound vector circular array sound pressure vibration velocity

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Acoustic vector circular array source number detection method based on characteristic value multiple threshold correction
  • Acoustic vector circular array source number detection method based on characteristic value multiple threshold correction
  • Acoustic vector circular array source number detection method based on characteristic value multiple threshold correction

Examples

Experimental program
Comparison scheme
Effect test

example

[0089] Assume that the number of sound vector uniform circular array elements is 12, the radius of the array is r=0.7λ, the incident direction of the signal source is 60°, the number of snapshots is 1k, and the specified observation direction is 60°. The detection factor D(L) of the covered circle detection criterion (GDE) is set to 1.

[0090] image 3 It is the analysis result of the influence of sound pressure and vector processing methods on detection performance, and the anti-noise performance of sound pressure and vibration velocity combined processing is better than that of sound pressure and vector independent processing methods. Figure 4 It is the simulation analysis result of the detection performance of different algorithms, and the detection performance of the method of the present invention is better than other methods under low signal-to-noise ratio. Figure 5 It is the simulation analysis result of multi-target detection performance, and the detection performa...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention belongs to the acoustic vector sensor array signal processing field, and more specifically relates to an acoustic vector circular array source number detection method based on characteristic value multiple threshold correction which is applied to underwater target remote passive detection. The method comprises establishing an acoustic vector circular array signal receiving model, obtaining acoustic vector circular array receiving acoustic pressure data, radial vibration velocity data and tangential vibration velocity, constructing a covariance matrix of acoustic vector circular array acoustic pressure and vibration velocity combined treatment, and decomposing characteristic values; and performing multiple threshold division treatment on a characteristic value set obtained by decomposing the covariance matrix to obtain a signal and noise corresponding characteristic value set. The method dynamically integrates an information theory detection method based on characteristic value multiple thresholds and the sound anti-noise performance of an acoustic vector circular array, obviously reduces the signal to noise ratio threshold of a detection algorithm, and overcomes the disadvantages that traditional detection methods such as MDL, diagonal loading MDL, and GDE are more sensitive to noise characteristic value change.

Description

technical field [0001] The invention belongs to the field of acoustic vector sensor array signal processing, and in particular relates to an acoustic vector circular array signal source number detection method based on eigenvalue multi-threshold correction, which is applied to remote passive detection of underwater targets. Background technique [0002] Estimation of the number of sources is an important issue in array signal processing. High-resolution spatial spectrum estimation technology generally needs to estimate the number of sources accurately first, otherwise it will lead to a decrease in the performance of the azimuth estimation algorithm. Therefore, in the fields of radar, sonar, communication, etc. Has a wide range of applications. [0003] With the continuous development of acoustic vector sensor technology, vector hydrophones are widely used in various fields of underwater acoustic engineering. Vector hydrophone array signal processing can effectively improve ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G01S3/80
CPCG01S3/80
Inventor 时胜国李赢祝文昭朱中锐时洁胡博张昊阳莫世奇张揽月方尔正
Owner HARBIN ENG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products