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distributed particle filter based speaker tracking in distributed microphone networks under non-Gaussian noise environments

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DIGITAL SIGNAL PROCESSING 2017年 63卷 112-122页

作者： Wang, Ruifang Chen, Zhe Yin, Fuliang Zhang, Qiaoling Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Shenyang Aerosp Univ Sch Elect Informat Engn Shenyang Peoples R China

Non-Gaussian noise distorts the speech signals and leads to the degradation of speaker tracking performance. In this paper, a distributed particle filter (DPF) based speaker tracking method in distributed microphone networks under non-Gaussian noise and reverberant environments is proposed. A generalized correntropy function is first presented to estimate the time differences of arrival (TDOA) for speech signals at each node in distributed microphone networks. Next, to address spurious TDOA estimations caused by noise and reverberation, a multiple-hypothesis likelihood model is introduced to calculate the local likelihood functions of the DPF. Finally, a DPF fusing local likelihood functions with an average consensus algorithm is employed to estimate a moving speaker's positions. The proposed method can accurately track the speaker under non-Gaussian noise and reverberant environments, and it is scalable and robust against the nodes failure in distributed networks. Simulation experiments show the validation of the proposed speaker tracking method. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Speaker tracking distributed particle filter distributed microphone networks Non-Gaussian noise Generalized correntropy function

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distributed particle filter using Pairwise K-Selective Gossip for Cognitive Radio Positioning 19

Distributed Particle Filter using Pairwise K-Selective Gossi...

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19th International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Zhao, Yubin Li, Xiaofan Zhang, Sha Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Shenzhen Yaoyuan Technol Limited Shenzhen 518055 Peoples R China Shenzhen Inst Radio Testing & Tech Shenzhen 518000 Peoples R China

ISBN: (纸本)9784904020098

The cognitive radio positioning system requires a distributed localization algorithm, since the secondary uses (SUs) can not communication with primary users (PUs) directly. The localization algorithm should also be robust to the topology changes of SUs due to the opportunistic access nature. In this paper, we propose a distributed particle filtering method to locate the PU's position. The ranging measurement of PU is obtained through the extended sensing strategy (ESS). The particles for each node is generated locally and transmitted via a K-selective gossip algorithm. Our algorithm is implemented without a topology management scheme and the communication overhead is lower than other distributed particle filter schemes. Simulations demonstrate that the estimation performance of our algorithm is accurate and approach to the centralized scheme with low communication cost.

关键词： cognitive radio network extended sensing strategy distributed particle filter gossip algorithm

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Target tracking based on a distributed particle filter in underwater sensor networks

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2008年第8期8卷 1023-1033页

作者： Huang, Yan Liang, Wei Yu, Hai-bin Xiao, Yang Chinese Acad Sci Key Lab Ind Informat Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Grad Sch Beijing 100049 Peoples R China Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA

In this paper based oil a distributed particle filter. two tracking algorithms are proposed for tracking mobile targets in cluster-based underwater sensor networks (USNs). Both tracking algorithms run local particle filter sequentially at each cluster along target trajectories, but they adopt different methods of selecting measurements from sensor nodes to balance the information contribution against the cost. Performance metrics are proposed and discussed ill terms of tracking performance, communication cost, energy cost, and tracking response time. Simulations are conducted to quantitatively compare the proposed algorithms as well as another tracking algorithm based oil extended Kalman filter (EKF). Our results indicate that one tracking algorithm achieves hi-her tracking, accuracy while the other achieves dramatic reduction of communication cost, energy cost, and tracking response time. Furthermore. performance of two tracking algorithms has been Studied in terms of detection threshold and sensor density. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： underwater sensor networks distributed particle filter target tracking performance evaluation

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A distributed particle filter for Bearings-Only Tracking on Spherical Surfaces

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IEEE SIGNAL PROCESSING LETTERS 2016年第3期23卷 326-330页

作者： Yu, Jun Ye Coates, Mark J. Rabbat, Michael G. Blouin, Stephane McGill Univ ECE Dept Montreal PQ H3A 0E9 Canada Def Res & Dev Canada Atlantic Res Ctr Dartmouth NS B2Y 3Z7 Canada

We present a distributed particle filter for bearings-only tracking of a target moving on the surface of a sphere, such as Earth. The proposed filter accounts for the curvature of the surface in the measurement model for more robust performance. In addition, a linearization of the likelihood function significantly reduces the communication overhead. Simulations demonstrate that the proposed distributed approach maintains accuracy comparable to that of a centralized filter with access to all measurements even when the sensors and target are spread over a large region, where a planar approximation would fail.

关键词： Bearings-only tracking distributed particle filter gossip algorithms likelihood approximation

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Speaker Tracking Based on distributed particle filter and Iterative Covariance Intersection in distributed Microphone Networks

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2019年第1期13卷 76-87页

作者： Wang, Ruifang Chen, Zhe Yin, Fuliang Dalian Univ Technol Sch Informat & Commun Engn Dalian 116023 Peoples R China Shenyang Aerosp Univ Sch Elect & Informat Engn Shenyang 110136 Liaoning Peoples R China

Speaker tracking in distributed microphone networks is a challenging task due to the adverse effects of reverberation and noise. In this paper, a speaker tracking method based on distributed particle filter (DPF) and modified iterative covariance intersection (MICI) algorithm is proposed in distributed microphone networks. Specifically, the time difference of arrival (TDOA) of speech signals received by a pair of m icmp hones at each node is first estimated using the generalized cross-correlation function. Next, multiple TDOAs are considered as the local measurement via a choice strategy and the multiple-hypothesis model is used as the local likelihood function of the DPF. Finally, the MICI algorithm is proposed to fuse local estimates to implement a global consistent speaker tracking where the local estimates at individual nodes are allowed to be unknown cross-correlations. The proposed method can successfully track the speaker in reverberant and noisy environments, and it is robust to the node faults and suitable to track speaker in networks with the unknown number of nodes. Simulation results demonstrate that the proposed method has better performance over the existing methods when SNR < 10 dB. Real-world experiments reveal validity of the proposed method.

关键词： Speaker tracking distributed particle filter distributed microphone networks iterative covariance intersection

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Speech Source Tracking Based on distributed particle filter in Reverberant Environments 3rd

Speech Source Tracking Based on Distributed Particle Filter ...

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3rd European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Wang, Ruifang Lan, Xiaoyu Shenyang Aerosp Univ Sch Elect & Informat Engn Shenyang 110136 Peoples R China Dalian Univ Technol Sch Informat & Commun Engn Dalian 116023 Peoples R China

ISBN: (纸本)9783030364052;9783030364045

In reverberant and noisy environments, tracking a speech source in distributed microphone networks is a challenging problem. A speech source tracking method based on distributed particle filter (DPF) and average consensus algorithm (ACA) is proposed in distributed microphone networks. The generalized cross-correlation (GCC) function is used to approximate the time difference of arrival (TDOA) of speech signals received by two microphones at each node. Next, the multiple-hypothesis model based on multiple TDOAs is calculated as the local likelihood function of the DPF. Finally, the ACA is applied to fuse local state estimates from local particle filter (PF) to obtain a global consensus estimate of the speech source at each node. The proposed method can accurately track moving speech source in reverberant and noisy environments with distributed microphone networks, and it is robust against the node failures. Simulation results reveal the validity of the proposed method.

关键词： Speech source tracking distributed particle filter distributed microphone networks Average consensus

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An Adaptive Consensus Based distributed particle filter for Cooperative Object Tracking 36

An Adaptive Consensus Based Distributed Particle Filter for ...

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第36届中国控制会议

作者： Wentao Yu Xiaoyong Zhang Aibin Chen Kuo-chi Lin College of Computer and Information Engineering Central South University of Forestry and Technology School of Information Science and Engineering Central South University Department of Mechanical and Aerospace Engineering University of Central Florida

ISBN: (纸本)9781538629185

Object tracking is a fundamental skill of mobile *** a multi-robot system the object tracking accuracy of each robot is usually different because of the dynamic environment and the different sensing *** focus of cooperative object tracking is how to fuse different robots' observation to obtain more robust and accurate estimation of the *** paper investigates this problem using an adaptive consensus based distributed particle filter *** this algorithm,each robot runs a particle filter to achieve local object *** a consensus algorithm which can establish an agreement between all robots is utilized to generalize a global posterior about the object *** fully consider the difference between each robot's object tracking accuracy,Renyi entropy is used to adaptively adjust the weight of the consensus *** concept of Renyi entropy is utilized to measure the distance between a robot's local posterior and the global posterior and then a weight will be assigned to the robot according to the Renyi *** weight will be used continuously in the next consensus *** the proposed algorithm in this paper the accuracy and the robust of the cooperative can be *** the effectiveness of the proposed algorithm is verified by simulation results.

关键词： consensus distributed particle filter Renyi entropy cooperative object tracking

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Speaker tracking based on distributed particle filter and interacting multiple model in distributed microphone networks

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APPLIED ACOUSTICS 2021年 174卷 107741-107741页

作者： Wang, Ruifang Chen, Zhe Yin, Fuliang Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Shenyang Aerosp Univ Sch Elect Informat Engn Shenyang Peoples R China

The reverberations and Gaussian noises from indoor environments pollute speech signals and bring the degradation of speaker tracking performance. In the paper, a speaker tracking method based on the distributed particle filter (DPF) and the interacting multiple model (IMM) is proposed in distributed microphone networks. The generalized cross-correlation (GCC) function is first employed to estimate the time difference of arrival (TDOA) of speech signals received by two microphones at each node. To overcome the adverse effects of reverberation and noise, multiple TDOAs are selected to calculate the multiple-hypothesis model as the local weight of the DPF. To simulate a real speaker motion, the IMM algorithm is applied and a calculation method of the local measurement is presented based on multiple TDOAs. Finally, the speaker tracking method based on the DPF and IMM is performed to track a moving speaker and obtain a global consistent position estimate. The proposed method can track the moving speaker in reverberant and noisy environments with high tracking accuracy in distributed networks, and it is robust against the fault nodes. Simulation results demonstrate the validity of the proposed speaker tracking method. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Speaker tracking distributed particle filter distributed microphone networks Interacting multiple model

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Global Coherence Field and distributed particle filter-Based Speaker Tracking in distributed Microphone Networks

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JOURNAL OF COMPUTATIONAL ACOUSTICS 2015年第3期23卷 1550010-1550010页

作者： Zhang, Qiaoling Chen, Zhe Yin, Fuliang Dalian Univ Technol Sch Informat & Commun Engn Dalian 116023 Peoples R China

Based on the combination of global coherence field (GCF) and distributed particle filter (DPF) a speaker tracking method is proposed for distributed microphone networks in this paper. In the distributed microphone network, each node comprises a microphone pair, and its generalized cross-correlation (GCC) function is estimated. Based on the average over all local GCC observations, a global coherence field-based pseudo-likelihood (GCF-PL) function is developed as the likelihood for a DPF. In the proposed method, all nodes share an identical particle set, and each node performs local particle filtering simultaneously. In the local particle filter, the likelihood GCF-PL for each particle weight is computed with an average consensus algorithm. With an identical particle set and the consistent estimate of GCF-PL for each particle weight, all individual nodes possess a common particle presentation for the global posterior of the speaker state, which is utilized by each node for an estimated global speaker position. Employing the GCF-PL as the likelihood for DPF, no assumption is required about the independence of nodes observations as well as observation noise statistics. Additionally, only local information exchange occurs among neighboring nodes;and finally each node has a global estimate of the speaker position. Simulation results demonstrate the validity of the proposed method.

关键词： distributed microphone networks speaker tracking distributed particle filter global coherence field average consensus algorithm

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distributed Multiple Speaker Tracking Based on Unscented particle filter and Data Association in Microphone Array Networks

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2022年第2期41卷 933-955页

作者： Wang, Rong Chen, Zhe Yin, Fuliang Dalian Univ Technol Sch Informat & Commun Engn Dalian 116023 Peoples R China

Multiple speaker localization and tracking in noisy and reverberant environments is a challenging task. A distributed multiple speaker tracking method based on unscented particle filter and data association is proposed in microphone array networks. First, the generalized cross correlation (GCC) function is estimated at each node and the time difference of arrival (TDOA) is viewed as observation for tracking. Considering the ambiguity of the observations caused by the interference of other acoustic sources and multipath, the data association technique is used at each node to associate the available observations with each speaker. Then according to the characteristics of GCC function, the association probability of valid observations for each speaker is calculated by combining the amplitude of the valid observations with the statistical distance. After that, the local state of speakers is obtained at each node based on unscented particle filter. Finally, according to the reliability of the local state of each node, a dynamic weighted consensus fusion algorithm is presented to approximate the global state estimation and obtain good multiple speakers tracking performance. The proposed method can track multiple speakers under reverberant and noisy environments in a distributed manner and is scalable and robust against node failure in DMA. Simulation results verify the effectiveness of the proposed method.

关键词： Multiple speaker tracking Data association distributed particle filter Dynamic weighted consensus distributed microphone array (DMA)

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