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学者姓名:韩崇昭

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Dual Sensor Control Scheme for Multi-Target Tracking EI SCIE PubMed Scopus
期刊论文 | 2018 , 18 (5) | SENSORS
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Abstract :

Sensor control is a challenging issue in the field of multi-target tracking. It involves multi-target state estimation and the optimal control of the sensor. To maximize the overall utility of the surveillance system, we propose a dual sensor control scheme. This work is formulated in the framework of partially observed Markov decision processes (POMDPs) with Mahler's finite set statistics (FISST). To evaluate the performance associated with each control action, a key element is to design an appropriate metric. From a task-driven perspective, we utilize a metric to minimize the posterior distance between the sensor and the target. This distance-related metric promotes the design of a dual sensor control scheme. Moreover, we introduce a metric to maximize the predicted average probability of detection, which will improve the efficiency by avoiding unnecessary update processes. Simulation results indicate that the performance of the proposed algorithm is significantly superior to the existing methods.

Keyword :

POMDPs FISST-based filter sensor control multi-target tracking

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GB/T 7714 Li, Wei , Han, Chongzhao . Dual Sensor Control Scheme for Multi-Target Tracking [J]. | SENSORS , 2018 , 18 (5) .
MLA Li, Wei 等. "Dual Sensor Control Scheme for Multi-Target Tracking" . | SENSORS 18 . 5 (2018) .
APA Li, Wei , Han, Chongzhao . Dual Sensor Control Scheme for Multi-Target Tracking . | SENSORS , 2018 , 18 (5) .
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A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System EI SCIE Scopus
期刊论文 | 2018 | MATHEMATICAL PROBLEMS IN ENGINEERING
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To track multiple extended targets for the nonlinear system, this paper employs the idea of the particle filter to track kinematic states and shape formation of extended targets. First, the Bayesian framework is proposed for multiple extended targets to jointly estimate multiple extended target state and association hypothesis. Furthermore, a joint proposal distribution is defined for the multiple extended target state and association hypothesis. Then, the Bayesian framework of multiple extended target tracking is implemented by the particle filtering which could release the high computational burden caused by the increase in the number of extended targets and measurements. Simulation results show that the proposed multiple extended target particle filter has superior performance in shape estimation and improves the performance of the position estimation in the situation that there are spatially closed extended targets.

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GB/T 7714 Han, Yulan , Han, Chongzhao . A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System [J]. | MATHEMATICAL PROBLEMS IN ENGINEERING , 2018 .
MLA Han, Yulan 等. "A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System" . | MATHEMATICAL PROBLEMS IN ENGINEERING (2018) .
APA Han, Yulan , Han, Chongzhao . A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System . | MATHEMATICAL PROBLEMS IN ENGINEERING , 2018 .
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Constrained Multi-Sensor Control Using a Multi-Target MSE Bound and a delta-GLMB Filter EI SCIE PubMed Scopus
期刊论文 | 2018 , 18 (7) | SENSORS
WoS CC Cited Count: 1 SCOPUS Cited Count: 1
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Abstract :

The existing multi-sensor control algorithms for multi-target tracking (MTT) within the random finite set (RFS) framework are all based on the distributed processing architecture, so the rule of generalized covariance intersection (GCI) has to be used to obtain the multi-sensor posterior density. However, there has still been no reliable basis for setting the normalized fusion weight of each sensor in GCI until now. Therefore, to avoid the GCI rule, the paper proposes a new constrained multi-sensor control algorithm based on the centralized processing architecture. A multi-target mean-square error (MSE) bound defined in our paper is served as cost function and the multi-sensor control commands are just the solutions that minimize the bound. In order to derive the bound by using the generalized information inequality to RFS observation, the error between state set and its estimation is measured by the second-order optimal sub-pattern assignment metric while the multi-target Bayes recursion is performed by using a 6-generalized labeled multi-Bernoulli filter. An additional benefit of our method is that the proposed bound can provide an online indication of the achievable limit for MTT precision after the sensor control. Two suboptimal algorithms, which are mixed penalty function (MPF) method and complex method, are used to reduce the computation cost of solving the constrained optimization problem. Simulation results show that for the constrained multi-sensor control system with different observation performance, our method significantly outperforms the GCI-based Cauchy-Schwarz divergence method in MTT precision. Besides, when the number of sensors is relatively large, the computation time of the MPF and complex methods is much shorter than that of the exhaustive search method at the expense of completely acceptable loss of tracking accuracy.

Keyword :

multi-sensor control Bayesian estimation error bounds labeled random finite set multi-target tracking

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GB/T 7714 Lian, Feng , Hou, Liming , Liu, Jing et al. Constrained Multi-Sensor Control Using a Multi-Target MSE Bound and a delta-GLMB Filter [J]. | SENSORS , 2018 , 18 (7) .
MLA Lian, Feng et al. "Constrained Multi-Sensor Control Using a Multi-Target MSE Bound and a delta-GLMB Filter" . | SENSORS 18 . 7 (2018) .
APA Lian, Feng , Hou, Liming , Liu, Jing , Han, Chongzhao . Constrained Multi-Sensor Control Using a Multi-Target MSE Bound and a delta-GLMB Filter . | SENSORS , 2018 , 18 (7) .
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An adaptive tracking algorithm for irregular shape extended target 自适应不规则形状扩展目标跟踪算法 EI Scopus CSCD PKU
期刊论文 | 2018 , 35 (8) , 1111-1119 | Kongzhi Lilun Yu Yingyong/Control Theory and Applications
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© 2018, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved. In view of the extended target tracking with irregular shape, this paper proposes an adaptive contour algorithm based on star-convex random hypersurface model in the case of unknown target priori shape and shape change in the process of target evolution. First, this paper studies the adaptive setting of Fourier coefficient of radial function, which is utilized to describe the star-convex shape. And then an adaptive method for irregular shape is proposed based on the centroid contour distance. Moreover, aiming at the sudden shape change in the process of target motion, this paper uses the sliding window to formulate detection statistics and proposes a real-time detection method shape change. Furthermore, an adaptive contour algorithm which can quickly approximate the real target shape is proposed to track extended target with shape change in real time. Finally, this paper proposes a quasi-Jaccard distance to evaluate the performance of shape estimation. Simulation results verify the effectiveness of the proposed algorithm.

Keyword :

Adaptive contour Extended target tracking Irregular shape Random hypersurface model Sliding window

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GB/T 7714 Chen, Hui , Du, Jin-Rui , Han, Chong-Zhao . An adaptive tracking algorithm for irregular shape extended target 自适应不规则形状扩展目标跟踪算法 [J]. | Kongzhi Lilun Yu Yingyong/Control Theory and Applications , 2018 , 35 (8) : 1111-1119 .
MLA Chen, Hui et al. "An adaptive tracking algorithm for irregular shape extended target 自适应不规则形状扩展目标跟踪算法" . | Kongzhi Lilun Yu Yingyong/Control Theory and Applications 35 . 8 (2018) : 1111-1119 .
APA Chen, Hui , Du, Jin-Rui , Han, Chong-Zhao . An adaptive tracking algorithm for irregular shape extended target 自适应不规则形状扩展目标跟踪算法 . | Kongzhi Lilun Yu Yingyong/Control Theory and Applications , 2018 , 35 (8) , 1111-1119 .
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A Novel Sensor Selection Approach With Bayes Framework for Target Tracking 基于贝叶斯理论框架的传感器选择算法 EI Scopus CSCD PKU
期刊论文 | 2018 , 44 (8) , 1425-1435 | Zidonghua Xuebao/Acta Automatica Sinica
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Abstract :

Copyright © 2018 Acta Automatica Sinica. All rights reserved. In large-scale sensor networks, a target tracking approach based on sensor selection is presented in the Bayes framework. The proposed approach mainly contains the following three steps. Firstly, the objective function is obtained in the Bayes framework. Then, the sensor selection strategy is adopted according to the objective function. Finally, tracking results are obtained by fusion of those selected sensors. Compared with the traditional target tracking approach, the proposed sensor selection approach is much easier and more reliable. Moreover, the clustered target tracking scenarios are considered in this research, thus so the proposed approach is robust for target tracking applications. Simulation results show the effectiveness of the proposed approach.

Keyword :

Bayes framework Information fusion Large-scale sensor network Sensor selection Target tracking

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GB/T 7714 Guo, Jun-Jun , Han, Chong-Zhao . A Novel Sensor Selection Approach With Bayes Framework for Target Tracking 基于贝叶斯理论框架的传感器选择算法 [J]. | Zidonghua Xuebao/Acta Automatica Sinica , 2018 , 44 (8) : 1425-1435 .
MLA Guo, Jun-Jun et al. "A Novel Sensor Selection Approach With Bayes Framework for Target Tracking 基于贝叶斯理论框架的传感器选择算法" . | Zidonghua Xuebao/Acta Automatica Sinica 44 . 8 (2018) : 1425-1435 .
APA Guo, Jun-Jun , Han, Chong-Zhao . A Novel Sensor Selection Approach With Bayes Framework for Target Tracking 基于贝叶斯理论框架的传感器选择算法 . | Zidonghua Xuebao/Acta Automatica Sinica , 2018 , 44 (8) , 1425-1435 .
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Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network SCIE PubMed
期刊论文 | 2018 , 18 (12) | SENSORS
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A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized delta-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to-noise ratio scenarios.

Keyword :

error bound sensor selection decentralized sensor network labeled random finite set multi-target tracking

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GB/T 7714 Lian, Feng , Hou, Liming , Wei, Bo et al. Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network [J]. | SENSORS , 2018 , 18 (12) .
MLA Lian, Feng et al. "Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network" . | SENSORS 18 . 12 (2018) .
APA Lian, Feng , Hou, Liming , Wei, Bo , Han, Chongzhao . Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network . | SENSORS , 2018 , 18 (12) .
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Two novel sensor control schemes for multi-target tracking via delta generalised labelled multi-Bernoulli filtering EI SCIE
期刊论文 | 2018 , 12 (9) , 1131-1139 | IET SIGNAL PROCESSING
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The study addresses the sensor control problem for multi- target tracking via delta generalised labelled multi- Bernoulli ( d- GLMB) filter, and proposes two novel single- sensor control schemes. One is that the Renyi divergence is used as the objective function to measure the information gain between the predicted and posterior densities of the d- GLMB filter, and it is superior for the overall performance of the system. Since most of the sensor control schemes, including the scheme the authors proposed, are faced the curse of computation, thus the other novel scheme is proposed. This scheme, in which the sum of the statistical distances between the predicted states of targets and sensor is used as the objective function, evades the updated step of the multi- target filter, when computing the objective function for each admissible action. Moreover, these two sensor control schemes are applied to a distributed multi- sensor system, in which the proposed schemes are used for each sensor node and the generalised covariance intersection method is used to compute the fused multi- target posterior density. Finally, they adopt the sequential Monte- Carlo method in bearing and range multi- target tracking scenarios to illustrate the effectiveness of the proposed methods.

Keyword :

distributed sensors delta generalised labelled multiBernoulli filtering Monte Carlo methods distributed multisensor system sensor node single-sensor control schemes fused multitarget posterior density sensor control problem multitarget filter target tracking tracking filters multi-target tracking

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GB/T 7714 Zhang, Guanghua , Lian, Feng , Han, Chongzhao et al. Two novel sensor control schemes for multi-target tracking via delta generalised labelled multi-Bernoulli filtering [J]. | IET SIGNAL PROCESSING , 2018 , 12 (9) : 1131-1139 .
MLA Zhang, Guanghua et al. "Two novel sensor control schemes for multi-target tracking via delta generalised labelled multi-Bernoulli filtering" . | IET SIGNAL PROCESSING 12 . 9 (2018) : 1131-1139 .
APA Zhang, Guanghua , Lian, Feng , Han, Chongzhao , Chen, Hui , Fu, Na . Two novel sensor control schemes for multi-target tracking via delta generalised labelled multi-Bernoulli filtering . | IET SIGNAL PROCESSING , 2018 , 12 (9) , 1131-1139 .
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A Positive Approximation Set Based Accelerating Approach for Condition Attribute Reduction CPCI-S
会议论文 | 2018 , 198-205 | 11th IEEE/ACM International Conference on Utility and Cloud Computing (UCC-Companion) / 5th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
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Abstract :

In this paper, the specific framework and characteristics of positive approximation set are investigated. A novel attribute reduction algorithm is introduced for accelerating the existing reduction approaches for incomplete decision table. Consequently, a tolerance relation based algorithm (IFSPA) is proposed and employed to improve the performance of existing heuristic reduction algorithms. A series of experiments using real-life data sets are carried out to verify the effectiveness of the improved algorithms and compare their efficiency with that of the original algorithms. The simulation results prove the fact that the improved algorithms would output exactly the same reducts as the original ones, while the improved ones could accomplish the reduction task in a shorter time and even in a more stable method.

Keyword :

attribute reduction positive approximation set rough set incomplete decision table

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GB/T 7714 Yan, Tao , Han, Chongzhao , Wang, Chengnan . A Positive Approximation Set Based Accelerating Approach for Condition Attribute Reduction [C] . 2018 : 198-205 .
MLA Yan, Tao et al. "A Positive Approximation Set Based Accelerating Approach for Condition Attribute Reduction" . (2018) : 198-205 .
APA Yan, Tao , Han, Chongzhao , Wang, Chengnan . A Positive Approximation Set Based Accelerating Approach for Condition Attribute Reduction . (2018) : 198-205 .
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A novel sensor selection algorithm for Multi-Target Tracking in Wireless Sensor Networks CPCI-S
会议论文 | 2018 , 2854-2858 | Chinese Automation Congress (CAC)
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Sensor selection is a very challenging issue where the choice of appropriate sensors should be made during the tracking processing. In order to improve the utilization of the wireless sensor networks, this paper presents an adaptive selection approach. The proposed method is dedicated to dynamically adjusting the number and the state of sensors involved. To avoid the lag of the sensor selection process, the partially observed Markov decision process (POMDP) is introduced. Since the multi-sensor is involved, we design an intuitive fusion process for the state estimation. Besides, the metric that maximizes the probability of detection is introduced to evaluate the optimal combination of sensors. A typical multi-target tracking scenario is studied in a wireless sensor networks. The results verify the effectiveness of algorithm in the selection of the sensors.

Keyword :

information fusion wireless sensor networks sensor selection target tracking

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GB/T 7714 Li, Wei , Han, Chongzhao . A novel sensor selection algorithm for Multi-Target Tracking in Wireless Sensor Networks [C] . 2018 : 2854-2858 .
MLA Li, Wei et al. "A novel sensor selection algorithm for Multi-Target Tracking in Wireless Sensor Networks" . (2018) : 2854-2858 .
APA Li, Wei , Han, Chongzhao . A novel sensor selection algorithm for Multi-Target Tracking in Wireless Sensor Networks . (2018) : 2854-2858 .
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A Novel Approach of Sensor Management Based on Compressive Sensing in Sensor Network CPCI-S
会议论文 | 2018 , 3482-3485 | Chinese Automation Congress (CAC)
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Sensor management is more and more important in simultaneous tracking and identification with multiple sensors. In this paper, we proposed a novel sensor management approach based on compressed sensing for target tracking in sensor networks, to determine the subset of sensors with the most informative data. With the proposed approach, we consider original sensor management problem as a constrained optimization problem, where the goal is to determine the optimal values of probabilities so that the trace of the Fisher information matrix at any given time step can get its maximum. We also derived the formulations for the Fisher information matrix in case that the fusion center has complete measurement sparsity information. Experimental results show that the proposed approach consume fewer energy than the optimal scenario in which all sensor observations are transmitted to the fusion center.

Keyword :

sensor management sensor network compressive sensing target tracking

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GB/T 7714 Yan Tao , Han Chongzhao . A Novel Approach of Sensor Management Based on Compressive Sensing in Sensor Network [C] . 2018 : 3482-3485 .
MLA Yan Tao et al. "A Novel Approach of Sensor Management Based on Compressive Sensing in Sensor Network" . (2018) : 3482-3485 .
APA Yan Tao , Han Chongzhao . A Novel Approach of Sensor Management Based on Compressive Sensing in Sensor Network . (2018) : 3482-3485 .
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