Inverse problem with beamforming regularization matrix applied to sound source localization in closed wind-tunnel using microphone array

2014 ◽  
Vol 333 (25) ◽  
pp. 6858-6868 ◽  
Author(s):  
Thomas Padois ◽  
Philippe-Aubert Gauthier ◽  
Alain Berry
Keyword(s):  
Inverse Problem ◽  
Wind Tunnel ◽  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Sound Source Localization ◽  
Regularization Matrix

scholarly journals Arbitrary Microphone Array Optimization Method Based on TDOA for Specific Localization Scenarios

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4326 ◽  
Author(s):  
Haitao Liu ◽  
Thia Kirubarajan ◽  
Qian Xiao
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Optimization Model ◽  
Microphone Array ◽  
Optimization Method ◽  
Sound Source Localization ◽  
Positioning Accuracy ◽  
Specific Localization ◽  
Array Structures ◽  
Array Optimization

Various microphone array geometries (e.g., linear, circular, square, cubic, spherical, etc.) have been used to improve the positioning accuracy of sound source localization. However, whether these array structures are optimal for various specific localization scenarios is still a subject of debate. This paper addresses a microphone array optimization method for sound source localization based on TDOA (time difference of arrival). The geometric structure of the microphone array is established in parametric form. A triangulation method with TDOA was used to build the spatial sound source location model, which consists of a group of nonlinear multivariate equations. Through reasonable transformation, the nonlinear multivariate equations can be converted to a group of linear equations that can be approximately solved by the weighted least square method. Then, an optimization model based on particle swarm optimization (PSO) algorithm was constructed to optimize the geometric parameters of the microphone array under different localization scenarios combined with the spatial sound source localization model. In the optimization model, a reasonable fitness evaluation function is established which can comprehensively consider the positioning accuracy and robustness of the microphone array. In order to verify the array optimization method, two specific localization scenarios and two array optimization strategies for each localization scenario were constructed. The optimal array structure parameters were obtained through numerical iteration simulation. The localization performance of the optimal array structures obtained by the method proposed in this paper was compared with the optimal structures proposed in the literature as well as with random array structures. The simulation results show that the optimized array structure gave better positioning accuracy and robustness under both specific localization scenarios. The optimization model proposed could solve the problem of array geometric structure design based on TDOA and could achieve the customization of microphone array structures under different specific localization scenarios.

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