P-norm noise source positioning identification method based on weight correction parameter

A technology for positioning identification and parameter correction, applied in positioning, measuring devices, instruments, etc., can solve the problems that the relative size of the contribution of the sound source cannot be truly reflected, and the sound source identification cannot be achieved, so as to achieve accurate estimation and good stability

Inactive Publication Date: 2017-05-10
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

However, this method can only obtain the estimation result of the sound source orientation, and cannot truly reflec

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  • P-norm noise source positioning identification method based on weight correction parameter
  • P-norm noise source positioning identification method based on weight correction parameter
  • P-norm noise source positioning identification method based on weight correction parameter

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Embodiment

[0110] Simulation analysis:

[0111] In order to compare the effectiveness of the P-norm noise source localization algorithm based on weighted correction parameters (P-EVD), the localization and identification results of different types of sound sources were compared with the classical conventional beamforming technique (CBF) and subspace MUSIC algorithm. ;A 7×7 uniform grid array composed of acoustic microphones is adopted, and the array element spacing is 0.3m; the target sound source is located on the x-y plane, and the scanning area is 8m×8m; the scanning interval is 0.25m; the distance between the base array surface and the scanning surface is z=5m ; The background noise is Gaussian white noise, unless otherwise specified, the signal-to-noise ratio snr = 20dB; the number of Monte-Carlo experiments is 50; P = 1; the regularization parameter α is 1% of the maximum eigenvalue.

[0112] Considering that most noise sources can be regarded as distributed sound sources composed ...

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Abstract

The invention discloses a P-norm noise source positioning identification method based on a weight correction parameter, relating to the technical field of noise source positioning. The positioning identification method includes step 1 eigenvalue decomposition sound source positioning recognition method; step 2 P-norm noise source signal reconstruction method; and step 3 selection of algorithm for weight correction parameter Epsilon. The characteristic subspace of each order and the subspace response function are obtained by characteristic decomposition, a subspace sound source vector reconstruction model is established for the characteristic subspace of each order, the reconstruction model is solved by the P-norm sparsity constraint of the weight correction parameter, the subspace sound source vector is forced to converge faster and more efficiently to the location of the real sparse source, and the more concentrated sparse solution of energy can be obtained; the effects of signal-to-noise ratio, measurement distance, array aperture and analysis frequency on the algorithm positioning performance are analyzed, the simulation result shows that the method can achieve the accurate estimation of the position and amplitude of different types of sound sources, and the stability is good.

Description

technical field [0001] The invention relates to a P-norm high-resolution and high-precision noise source location identification method based on weighted correction parameters, and belongs to the technical field of noise source location. Background technique [0002] Conventional beamforming (conventional beamforming, CBF) as the most important noise source array signal processing technology is widely used in the detection and positioning of aircraft and automobile radiation noise because of its excellent tolerance and ease of operation. However, the spatial resolution of conventional beamforming is limited by the physical aperture of the array, that is, there is a "Rayleigh limit (Rayleigh)", and the problem of spatial "confusion" is serious in multi-sound source detection. [0003] In order to break through the "Rayleigh limit", many high-resolution spectral estimation methods have emerged, among which the multiple signal classification in subspace (MUSIC) algorithm is the...

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Application Information

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IPC IPC(8): G01S5/22
CPCG01S5/22
Inventor 刘月婵孙超牛訦琛安海琴常嘉文
Owner HARBIN UNIV OF SCI & TECH
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