• Complex
  • Title
  • Author
  • Keyword
  • Abstract
  • Scholars
High Impact Results & Cited Count Trend for Year Keyword Cloud and Partner Relationship

Query:

学者姓名:韩崇昭

Refining:

Source

Submit Unfold

Co-Author

Submit Unfold

Language

Submit

Clean All

Export Sort by:
Default
  • Default
  • Title
  • Year
  • WOS Cited Count
  • Impact factor
  • Ascending
  • Descending
< Page ,Total 79 >
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
Abstract&Keyword Cite

Abstract :

© 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

Cite:

Copy from the list or Export to your reference management。

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 等. "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 .
Export to NoteExpress RIS BibTex
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
Abstract&Keyword Cite

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

Cite:

Copy from the list or Export to your reference management。

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 等. "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 .
Export to NoteExpress RIS BibTex
Dual Sensor Control Scheme for Multi-Target Tracking EI SCIE PubMed Scopus
期刊论文 | 2018 , 18 (5) | SENSORS
Abstract&Keyword Cite

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

Cite:

Copy from the list or Export to your reference management。

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) .
Export to NoteExpress RIS BibTex
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
Abstract&Keyword Cite

Abstract :

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

Cite:

Copy from the list or Export to your reference management。

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 .
Export to NoteExpress RIS BibTex
Ensemble clustering based on Evidence theory EI CPCI-S Scopus
会议论文 | 2017 , 759-767 | 20th International Conference on Information Fusion (Fusion)
Abstract&Keyword Cite

Abstract :

Ensemble clustering consists in combining multiple clustering solutions into a single one, called the consensus, which can produce a more accurate and robust clustering of the data. In this paper, we attempt to implement ensemble clustering using Dempster-Shafer evidence theory. Individual clustering solutions are obtained using evidence theory and a novel diversity measure is proposed using the distance of evidence for selecting complementary individual solutions. After establishing the correspondence among different clustering solutions' labels, the consensus clustering solution can be obtained using evidence combination. Experimental results and related analyses show that our proposed approach can effectively implement the ensemble clustering.

Keyword :

Clustering Ensemble Clustering Distance of evidence Diversity Dempster-Shafer evidence theory

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Wang, Xueen , Han, Deqiang , Han, Chongzhao . Ensemble clustering based on Evidence theory [C] . 2017 : 759-767 .
MLA Wang, Xueen et al. "Ensemble clustering based on Evidence theory" . (2017) : 759-767 .
APA Wang, Xueen , Han, Deqiang , Han, Chongzhao . Ensemble clustering based on Evidence theory . (2017) : 759-767 .
Export to NoteExpress RIS BibTex
A Compressed Sensing Based Sensor Selection Algorithm for DOA Estimation CPCI-S CSSCI-E
会议论文 | 2017 , 30-35 | 20th International Conference on Information Fusion (Fusion)
Abstract&Keyword Cite

Abstract :

This paper presents a compressed sensing based sensor selection algorithm for direction-of-arrival estimation, in a large scale randomly distributed sensor array. First, a target tracker is employed for the prior information of target position. Second, according to the prior information, we reduce rank of sensing matrix by narrowing the interesting area. Third, a linear independence combination of sensors is utilized to represent the whole sensor array through eigendecomposition. Finally, the statistic and dynamic simulations are carried out, which demonstrate the feasibility of the proposed algorithm.

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Zeng, Linghao , Liu, Ling , Han, Chongzhao . A Compressed Sensing Based Sensor Selection Algorithm for DOA Estimation [C] . 2017 : 30-35 .
MLA Zeng, Linghao et al. "A Compressed Sensing Based Sensor Selection Algorithm for DOA Estimation" . (2017) : 30-35 .
APA Zeng, Linghao , Liu, Ling , Han, Chongzhao . A Compressed Sensing Based Sensor Selection Algorithm for DOA Estimation . (2017) : 30-35 .
Export to NoteExpress RIS BibTex
Sensor Management for Multi-Target Detection and Tracking Based on PCRLB EI CPCI-S Scopus
会议论文 | 2017 , 136-141 | 20th International Conference on Information Fusion (Fusion)
Abstract&Keyword Cite

Abstract :

This article presents an information theory based sensor management method to be used for aerospace multi-target collaborative detection and tracking. The proposed sensor management method follows an information theoretic approach, in which PCRLB is used to calculate the tracking accuracy of multi-target. The detection particles are employed to determine the detection probability of incoming targets. Moreover, BPSO is adopted as well to find the optimal allocation scheme of sensors for maximizing the tracking accuracy and detection probability. The results of simulation experiments demonstrate that the proposed method succeeds in assigning sensors optimally in region surveillance task.

Keyword :

binary particle swarm optimization sensor management particle filtering posterior Cramer-Rao lower bound

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Yan Tao , Han Chongzhao . Sensor Management for Multi-Target Detection and Tracking Based on PCRLB [C] . 2017 : 136-141 .
MLA Yan Tao et al. "Sensor Management for Multi-Target Detection and Tracking Based on PCRLB" . (2017) : 136-141 .
APA Yan Tao , Han Chongzhao . Sensor Management for Multi-Target Detection and Tracking Based on PCRLB . (2017) : 136-141 .
Export to NoteExpress RIS BibTex
Clustering based Box-Particle Probability Hypothesis Density filtering EI CPCI-S Scopus
会议论文 | 2017 , 206-212 | 20th International Conference on Information Fusion (Fusion)
Abstract&Keyword Cite

Abstract :

This paper investigates the box-particle filter for multi-target tracking, and proposes a clustering based box-particle implementation of PHD filter. A subdivision step is added before the estimation of states. Each box is divided into several sub-box based on the estimated number of targets. An equivalent set of particles can be extracted from the set of subdivided boxes. Then, clustering technique is applied to get the states from the particle set. Compared to the conventional method, this operation avoids the hard division of which target one specific box-particle belongs to. The proposed algorithm can improve the performance of the tracking result, especially for the circumstances some of the targets are close to each other in the surveillance area. Simulation results verify the effectiveness of the algorithm.

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Li, Wei , Han, Chongzhao . Clustering based Box-Particle Probability Hypothesis Density filtering [C] . 2017 : 206-212 .
MLA Li, Wei et al. "Clustering based Box-Particle Probability Hypothesis Density filtering" . (2017) : 206-212 .
APA Li, Wei , Han, Chongzhao . Clustering based Box-Particle Probability Hypothesis Density filtering . (2017) : 206-212 .
Export to NoteExpress RIS BibTex
Entropy Based Attribute Reduction Approach for Incomplete Decision Table EI CPCI-S Scopus
会议论文 | 2017 , 947-954 | 20th International Conference on Information Fusion (Fusion)
Abstract&Keyword Cite

Abstract :

in this paper, a new entropy based uncertainty measure is introduced for evaluating the significance of subsets of attributes in incomplete decision tables. Some properties of rough conditional entropy are derived. And three attribute reduction algorithms are provided, including an algorithm using exhaustive search, an algorithm using heuristic search and an algorithm using probabilistic search for incomplete decision tables. Furthermore, several simulation experiments on real incomplete data sets are carried out to assess the efficiency of the proposed algorithms. The final simulation results indicate that two of above algorithms can give satisfying performances in the procedure of attribute reduction for incomplete decision tables.

Keyword :

Attribute reduction conditional entropy rough set theory incomplete decision table

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Yan Tao , Han Chongzhao . Entropy Based Attribute Reduction Approach for Incomplete Decision Table [C] . 2017 : 947-954 .
MLA Yan Tao et al. "Entropy Based Attribute Reduction Approach for Incomplete Decision Table" . (2017) : 947-954 .
APA Yan Tao , Han Chongzhao . Entropy Based Attribute Reduction Approach for Incomplete Decision Table . (2017) : 947-954 .
Export to NoteExpress RIS BibTex
Data fusion and bias registration based on sensor selection for large-scale sensor networks EI CPCI-S Scopus
会议论文 | 2017 , 7286-7291 | 29th Chinese Control And Decision Conference (CCDC)
Abstract&Keyword Cite

Abstract :

This paper presents a new target tracking and the possible changing bias registration approach based on sensor selection for large-scale distributed sensor networks. We try to address this target tracking problem at the following three steps. Firstly, local-level tracking is addressed based on the estimated sensor biases for each sensor, and only the state estimates are transmitted to the fusion center; Secondly, data fusion is carried out by using the sensor selection approach at the fusion center, target state is estimated based on the tracking results reported by the selected sensors; Finally, sensors' biases are updated at the fusion center. In addition, both of sensor coverage problem and the possible changing bias problem are considered in our paper. The proposed approach only needs to select a small number of sensors for tracking, rather than the traditional approaches, which prefer to use all of the sensors. Simulation results show the effectiveness of the proposed approach.

Keyword :

sensor selection sensor registration large-scale sensor networks data fusion

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Guo, Junjun , Han, Chongzhao , Li, Longfei . Data fusion and bias registration based on sensor selection for large-scale sensor networks [C] . 2017 : 7286-7291 .
MLA Guo, Junjun et al. "Data fusion and bias registration based on sensor selection for large-scale sensor networks" . (2017) : 7286-7291 .
APA Guo, Junjun , Han, Chongzhao , Li, Longfei . Data fusion and bias registration based on sensor selection for large-scale sensor networks . (2017) : 7286-7291 .
Export to NoteExpress RIS BibTex
10| 20| 50 per page
< Page ,Total 79 >

Export

Results:

Selected

to

Format:
FAQ| About| Online/Total:2207/51350894
Address:XI'AN JIAOTONG UNIVERSITY LIBRARY(No.28, Xianning West Road, Xi'an, Shaanxi Post Code:710049) Contact Us:029-82667865
Copyright:XI'AN JIAOTONG UNIVERSITY LIBRARY Technical Support:Beijing Aegean Software Co., Ltd.