In multimedia applications, it is common to employ acoustic sensors collectively to enhance signals and to locate sound sources. A direct problem can be formulated to locate sound sources from a set of known sensors. ...
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In multimedia applications, it is common to employ acoustic sensors collectively to enhance signals and to locate sound sources. A direct problem can be formulated to locate sound sources from a set of known sensors. In order to form the acoustic sensornetwork, it is important to locate the sensorarray locations first. However, unlike other networks in which direct time-of-arrival (TOA) measurements might be possible, acoustic distributednetwork can only obtain time-difference-of-arrival (TDOA) measures indirectly from various sound source anchors. While it is common to employ convex optimization techniques to localize sensor locations in a network with TOA information, it has not been studied properly when it comes to TDOAs. This article considers the microphone array localization problem in a distributed acoustic network with TDOA measurements. We formulate the inverse problem which applied the known source locations to identify the wireless array configuration and estimate the location for each array. The proposed method formulates a mixed semidefinite programming (SDP) and second-order cone programming (SOCP) relaxation model, and then the acoustic geometry is obtained by solving a linear optimal programming. Furthermore, the characteristics of the optimal solution are studied and exact relaxation conditions are given. Experimental results demonstrate that the proposed mixed model can successfully estimate the sensor locations in noisy and reverberant environments for 2-dimensional and 3-dimensional space, which outperforms other relaxation methods.
A distributed sensor array network is studied, where sub-arrays are placed on those distributed observation platforms. In this model, bearing-only source localization is characterized in terms of direction of arrival ...
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A distributed sensor array network is studied, where sub-arrays are placed on those distributed observation platforms. In this model, bearing-only source localization is characterized in terms of direction of arrival (DOA) if the sources are far from the entire network, while their locations in the predefined Cartesian coordinate system can be obtained for the near-field case. For wideband signals, the focusing algorithm is applied at each sub-array to form an equivalent single frequency signal model. Then, a compressive sensing (CS) based DOA estimation method employing the group sparsity concept is proposed for far-field sources with the information acquired by all the platforms processed as a whole. This concept is further extended to near field, and a group sparsity based method to localize the near-field sources is derived. The proposed solutions are applicable for both uncorrelated and coherent signals, and the corresponding Cramer-Rao Bounds (CRBs) are derived. Compared with the maximum likelihood estimator (MLE) of forming the final result through a fusion process, where separately estimated unreliable bearing result at even one observation platform would spoil the overall performance, improved performance is achieved by both proposed methods. It is noted that only the covariance matrix in lieu of data samples at each platform is required for centralized processing, and therefore the increase of the data exchange workload among platforms is rather limited.
The target localization problem for distributed sensor array networks where a sub-array is placed at each receiver is studied, and under the compressive sensing (CS) framework, a group sparsity based two-dimensional l...
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ISBN:
(纸本)9781479981311
The target localization problem for distributed sensor array networks where a sub-array is placed at each receiver is studied, and under the compressive sensing (CS) framework, a group sparsity based two-dimensional localization method is proposed. Instead of fusing the separately estimated angles of arrival (AOAs), it processes the information collected by all the receivers simultaneously to form the final target locations. Simulation results show that the proposed localization method provides a significant performance improvement compared with the commonly used maximum likelihood estimator (MLE).
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