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< Page ,Total 91 >
基于区间箱粒子多伯努利滤波器的传感器控制策略 EI CSCD
期刊论文 | 2021 , 47 (6) , 1428-1443 | 自动化学报
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Abstract :

多目标跟踪中的传感器控制本质上是一个最优非线性控制问题,其在理论分析和计算上极具挑战性.本文基于区间不确定性推理,利用箱粒子多伯努利滤波器提出了一种基于信息测度的传感器控制策略.首先,本文利用箱粒子实现多伯努利滤波器,并通过一组带有权值的箱粒子来表征多目标后验概率密度函数.其次,利用箱粒子的高斯分布假设,将多伯努利密度近似为高斯混合.随后,选择柯西施瓦兹(Cauchy-Schwarz,CS)散度作为评价函数,并详细推导了两个高斯混合之间的CS散度的求解公式,以此为基础提出相应的传感器控制策略.此外,作为一种对比方案,利用蒙特卡罗方法,本文还给出了通过对箱粒子进行混合均匀采样,进而通过点粒子求解CS散度的递推公式,并提出了相应的控制策略.最后,仿真实验验证了所提算法的有效性.

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GB/T 7714 陈辉 , 邓东明 , 韩崇昭 . 基于区间箱粒子多伯努利滤波器的传感器控制策略 [J]. | 自动化学报 , 2021 , 47 (6) : 1428-1443 .
MLA 陈辉 等. "基于区间箱粒子多伯努利滤波器的传感器控制策略" . | 自动化学报 47 . 6 (2021) : 1428-1443 .
APA 陈辉 , 邓东明 , 韩崇昭 . 基于区间箱粒子多伯努利滤波器的传感器控制策略 . | 自动化学报 , 2021 , 47 (6) , 1428-1443 .
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A fused intelligent computing approach using stock big data for near future trend prediction EI
会议论文 | 2020 , 113-116 | 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2019
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Abstract :

This research makes an attempt of using historical stock big data to predict near future trend of the stock. To fulfill this task, a novel approach based on fused intelligent computing is introduced and investigated. It is composited of four main parts, including data discretization, attribute reduction, classification and decision fusion. Further, one or two algorithms are adopted to realize specific function in each part, respectively. The given stock indexes are selected by the reduction algorithm of discernibility matrix, and the outputs of multiple classifiers are fused by the decision fusion algorithm. These processes and other ones are dedicated to enhancing the accuracy of stock trend prediction. To demonstrate the effectiveness of our approach, a variety of experimental simulations utilizing historical data of three stocks in NASDAQ are carried out, and the prediction accuracy of the proposed approach are compared as well. The experimental results prove that our approach could accomplish the prediction task with high accuracy. © 2019 Association for Computing Machinery.

Keyword :

Intelligent computing Big data Data reduction Forecasting

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GB/T 7714 Yan, Tao , Han, Chongzhao , Jia, Yong . A fused intelligent computing approach using stock big data for near future trend prediction [C] . 2020 : 113-116 .
MLA Yan, Tao 等. "A fused intelligent computing approach using stock big data for near future trend prediction" . (2020) : 113-116 .
APA Yan, Tao , Han, Chongzhao , Jia, Yong . A fused intelligent computing approach using stock big data for near future trend prediction . (2020) : 113-116 .
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Multi-Sensor Control Based on Multi-Target Mean Square Error Bound EI CSCD
期刊论文 | 2020 , 46 (10) , 2177-2190 | Acta Automatica Sinica
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Abstract :

The paper proposes a new constrained multi-sensorcontrol algorithm based on the centralized processing architecture. In this method, a multi-target mean-square error bound is served as cost function of sensor control. In order to derive the bound by using the information inequality, the error between state set and its estimation is measured by the 2nd-order optimal sub-pattern assignment metric while the multi-target Bayes recursion is performed by using aδ-generalized labeled multi-Bernoulli filter. Mixed penalty function 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 Cauchy-Schwarz divergence method in tracking precision. Besides, when the number of sensors is relatively large, the computation time of the mixed penalty function and complex methods is much shorter than that of the exhaustive search method at the expense of completely acceptable loss of tracking accuracy. Copyright ©2020 Acta Automatica Sinica. All rights reserved.

Keyword :

Constrained optimization Errors Mean square error Cost functions

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GB/T 7714 Lian, Feng , Hou, Li-Ming , Liu, Jing et al. Multi-Sensor Control Based on Multi-Target Mean Square Error Bound [J]. | Acta Automatica Sinica , 2020 , 46 (10) : 2177-2190 .
MLA Lian, Feng et al. "Multi-Sensor Control Based on Multi-Target Mean Square Error Bound" . | Acta Automatica Sinica 46 . 10 (2020) : 2177-2190 .
APA Lian, Feng , Hou, Li-Ming , Liu, Jing , Han, Chong-Zhao . Multi-Sensor Control Based on Multi-Target Mean Square Error Bound . | Acta Automatica Sinica , 2020 , 46 (10) , 2177-2190 .
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A multiple extended target multi-Bernouli filter based on Gaussian process regression model EI CSCD
期刊论文 | 2020 , 37 (9) , 1931-1943 | Control Theory and Applications
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Abstract :

In view of the tracking problem of multiple extended target with irregular shape in the complicated and uncertain environment, a Gaussian process regression (GPR) based multiple extended target multi-Bernoulli filter (GPR- ETCBMeMBer) algorithm is proposed in this article. Firstly, on the basis of modeling state set and measurement set of multiple extended target as multi-Bernoulli random finite set (MBer RFS) and Poisson RFS respectively by using finite set statistics (FISST), This article models the random hypersurface based filtering algorithm of multiple extended target via GPR approach. Then, this article derives in detail and proposes a Gaussian mixture (GM) implementation of the GPR- ETCBMeMBer filter via the cubature Kalman filter (CKF). Finally, the effectiveness of the proposed method is verified by the simulations of star-convex shape multiple extended target tracking and multiple group target tracking. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

Keyword :

Clutter (information theory) Gaussian distribution Regression analysis Set theory Target tracking Kalman filters Gaussian noise (electronic)

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GB/T 7714 Chen, Hui , Li, Guo-Cai , Han, Chong-Zhao et al. A multiple extended target multi-Bernouli filter based on Gaussian process regression model [J]. | Control Theory and Applications , 2020 , 37 (9) : 1931-1943 .
MLA Chen, Hui et al. "A multiple extended target multi-Bernouli filter based on Gaussian process regression model" . | Control Theory and Applications 37 . 9 (2020) : 1931-1943 .
APA Chen, Hui , Li, Guo-Cai , Han, Chong-Zhao , Du, Jin-Rui . A multiple extended target multi-Bernouli filter based on Gaussian process regression model . | Control Theory and Applications , 2020 , 37 (9) , 1931-1943 .
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A priori-based subarray selection algorithm for DOA estimation EI SCIE PubMed Scopus
期刊论文 | 2020 , 20 (16) , 1-20 | Sensors (Switzerland)
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A finer direction-of-arrival (DOA) estimation result needs a large and dense array; it may, however, encounter the mutual coupling effect, which degrades the performance of DOA estimation. There is a new approach to mitigating this effect by using a nonuniform array to achieve DOA estimation. In this paper, we consider a priori DOA estimation, which is easily obtained from tracking results. The a priori DOA requires us to pay close attention to the high possibility of where the DOA will appear; then, a weight according to the prior probability distribution of DOA is added to each direction, which leads the sensing matrix of DOA estimation to be near low-rank. Thus, according to the low-rank matrix approximation theory, an optimal low-rank approximate matrix is obtained and an algorithm is proposed to select the elements of the original array according to right singular vectors of the approximate matrix. After that, the impacts of different weights are analyzed, and a mixed weight is presented which has flexibility for common use. Finally, a number of numerical simulations are carried out, and the results verify the effectiveness of the proposed methods. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keyword :

Numerical methods Probability distributions Direction of arrival Approximation algorithms

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GB/T 7714 Zeng, Linghao , Zhang, Guanghua , Han, Chongzhao . A priori-based subarray selection algorithm for DOA estimation [J]. | Sensors (Switzerland) , 2020 , 20 (16) : 1-20 .
MLA Zeng, Linghao et al. "A priori-based subarray selection algorithm for DOA estimation" . | Sensors (Switzerland) 20 . 16 (2020) : 1-20 .
APA Zeng, Linghao , Zhang, Guanghua , Han, Chongzhao . A priori-based subarray selection algorithm for DOA estimation . | Sensors (Switzerland) , 2020 , 20 (16) , 1-20 .
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A Multiple Extended Target Multi-Bernouli Filter Based on Star-convex Random Hypersurface Model EI CSCD Scopus
期刊论文 | 2020 , 46 (5) , 909-922 | Acta Automatica Sinica
SCOPUS Cited Count: 3
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Abstract :

Considering the tracking of multi-extended target with irregular shape in complicated and uncertain environment, this paper proposes a multi-extended target multi-Bernoulli filtering algorithm based on star-convex random hypersurface model (RHM). First, in the framework of finite set statistics (FISST), the multi-Bernoulli random finite set (MBer-RFS) and Poisson-RFS are used to model multi-extended target state and measurement respectively, and then the extended target cardinality balanced multi-target multi-Bernoulli (ET-CBMeMBer) filter is given. Subsequently, using RHM to represent the measurement source distribution of any star-convex extended target, this paper proposes the cubature Kalman Gaussian mixture Star-convex multi-extended target multi-Bernoulli filter. Besides, this paper also gives a performance metric which can evaluate the irregular shape estimation of multi-extended target. Finally, the effectiveness of the proposed method is verified by the tracking simulations of multi-extended target and multi-group target with sudden shape change. Copyright © 2020 Acta Automatica Sinica. All rights reserved.

Keyword :

Set theory Target tracking Stars Kalman filters

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GB/T 7714 Chen, Hui , Du, Jin-Rui , Han, Chong-Zhao . A Multiple Extended Target Multi-Bernouli Filter Based on Star-convex Random Hypersurface Model [J]. | Acta Automatica Sinica , 2020 , 46 (5) : 909-922 .
MLA Chen, Hui et al. "A Multiple Extended Target Multi-Bernouli Filter Based on Star-convex Random Hypersurface Model" . | Acta Automatica Sinica 46 . 5 (2020) : 909-922 .
APA Chen, Hui , Du, Jin-Rui , Han, Chong-Zhao . A Multiple Extended Target Multi-Bernouli Filter Based on Star-convex Random Hypersurface Model . | Acta Automatica Sinica , 2020 , 46 (5) , 909-922 .
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Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter EI SCIE PubMed
期刊论文 | 2019 , 19 (12) | SENSORS | IF: 3.275
WoS CC Cited Count: 4
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Abstract :

The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms.

Keyword :

extended target tracking partitioning algorithm multiple extended target filter

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GB/T 7714 Han, Yulan , Han, Chongzhao . Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter [J]. | SENSORS , 2019 , 19 (12) .
MLA Han, Yulan et al. "Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter" . | SENSORS 19 . 12 (2019) .
APA Han, Yulan , Han, Chongzhao . Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter . | SENSORS , 2019 , 19 (12) .
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Decentralized Sensor Selection Based on Multi-Target MS-OSPA Lower Bound EI CSCD PKU
期刊论文 | 2019 , 47 (10) , 2158-2165 | Tien Tzu Hsueh Pao/Acta Electronica Sinica
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Abstract :

A sensor selection optimization algorithm is proposed for decentralized large-scale multi-target tracking network. In this method, the lower bound of mean square optimal sub-pattern assignment error between multi-target state set and its estimation is taken as optimized objective function while the rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. The coordinate descent method is proposed to compromise the computation cost and tracking accuracy. Simulations verify the effectiveness of our method under different signal-to-noise ratio scenarios. © 2019, Chinese Institute of Electronics. All right reserved.

Keyword :

Sensor networks Target tracking Clutter (information theory) Signal to noise ratio

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GB/T 7714 Lian, Feng , Zhang, Xiu-Li , Wei, Bo et al. Decentralized Sensor Selection Based on Multi-Target MS-OSPA Lower Bound [J]. | Tien Tzu Hsueh Pao/Acta Electronica Sinica , 2019 , 47 (10) : 2158-2165 .
MLA Lian, Feng et al. "Decentralized Sensor Selection Based on Multi-Target MS-OSPA Lower Bound" . | Tien Tzu Hsueh Pao/Acta Electronica Sinica 47 . 10 (2019) : 2158-2165 .
APA Lian, Feng , Zhang, Xiu-Li , Wei, Bo , Hou, Li-Ming , Han, Chong-Zhao , Wang, Wei . Decentralized Sensor Selection Based on Multi-Target MS-OSPA Lower Bound . | Tien Tzu Hsueh Pao/Acta Electronica Sinica , 2019 , 47 (10) , 2158-2165 .
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Multi-sensor distributed control strategy for multi-target tracking EI CSCD PKU
期刊论文 | 2019 , 36 (10) , 1585-1598 | Kongzhi Lilun Yu Yingyong/Control Theory and Applications
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Distributed sensor network technology plays an extremely important role in complex multi-target tracking system. Aiming at distributed sensor control problem in multi-sensor multi-target tracking, this paper proposes some multi-sensor control strategies information-based. First, a multi-sensor multi-Bernoulli filter is presented by using random finite set (RFS), and a multi-sensor multi-Bernoulli density is approximated by a set of parameterized multi-Bernoulli process. Further, through the sequential Monte Carlo implementation of the multi-Bernoulli filter, the sampling scheme is designed to sample the multi-Bernoulli density, and then the multi-target state space distribution is approximated by a set of weighted particles. Subsequently, the Bhattacharyya distance, as the reward function, is used for the decision making of independent and parallel multi-sensor control. As another important part, this paper proposes a multi-sensor control strategy based on multi-target tactical significance assessment, where the goal is to evaluate multi-target tactical significance and then track preferentially the maximum threat target. Finally, the simulations verify the effectiveness of the proposed algorithms. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

Keyword :

Sensor networks Target tracking Monte Carlo methods Clutter (information theory) Distributed parameter control systems Decision making

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GB/T 7714 Chen, Hui , Deng, Dong-Ming , Han, Chong-Zhao . Multi-sensor distributed control strategy for multi-target tracking [J]. | Kongzhi Lilun Yu Yingyong/Control Theory and Applications , 2019 , 36 (10) : 1585-1598 .
MLA Chen, Hui et al. "Multi-sensor distributed control strategy for multi-target tracking" . | Kongzhi Lilun Yu Yingyong/Control Theory and Applications 36 . 10 (2019) : 1585-1598 .
APA Chen, Hui , Deng, Dong-Ming , Han, Chong-Zhao . Multi-sensor distributed control strategy for multi-target tracking . | Kongzhi Lilun Yu Yingyong/Control Theory and Applications , 2019 , 36 (10) , 1585-1598 .
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A novel sensor selection algorithm for Multi-Target Tracking in Wireless Sensor Networks CPCI-S CSSCI-E
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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