In this article, wideband 3-D source localization utilizing only 1-D angle measurements based on a general distributed linear array (in lieu of planar array) network is studied. Instead of fusing the independent direc...
详细信息
In this article, wideband 3-D source localization utilizing only 1-D angle measurements based on a general distributed linear array (in lieu of planar array) network is studied. Instead of fusing the independent directions of arrival estimated by individual subarrays to form the final source location, a group sparsity-based localization method is proposed to exploit the information acquired by all receivers jointly with the corresponding closed-form Cram & eacute;r-Rao bound for 3-D localization derived. Simulation results demonstrate that improved performance and robustness is achieved by our proposed method.
The location estimation of multiple simultaneously active sources in an acoustic sensor network is quite challenging because the correct combination of measurements received from different microphone arrays is not usu...
详细信息
The location estimation of multiple simultaneously active sources in an acoustic sensor network is quite challenging because the correct combination of measurements received from different microphone arrays is not usually known, generally termed the data association problem. Existing techniques generally formulate the data association problem as a multidimensional assignment and solve them using classical optimal techniques. Although these approaches show effectiveness for a limited amount of active sources, the computational time and complexity of these techniques increase significantly when the microphones or acoustic sources increase. In this paper, a learning approach is proposed that solves the multidimensional assignment using a deep neural network. Initially, the features that are employed for the association are formulated for each of the detected sources in all the array nodes, and a multidimensional assignment problem is formulated. Subsequently, a deep multidimensional assignment network is devised to extract the correspondence probability of the measurements received from the microphone arrays. Specifically, a data-driven differentiable approach is presented for multidimensional assignments that is computationally efficient. The proposed methodology is validated under realistic conditions for both speech and urban signals. The methodology is compared to state-of-the-art methods for showing the performance gain in terms of accuracy of association and location estimation with a reduced computational time.(c) 2023 Elsevier Inc. All rights reserved.
暂无评论