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maneuvering target tracking algorithm based on CDKF in observation bootstrapping strategy

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High Technology Letters 2017年第2期23卷 149-155页

作者：胡振涛 Zhang Jin Fu Chunling Li Xian Instituteof Image Processing and Pattern Recognition Henan University Kaifeng 475004 P. R. China School of Physics and Electronics Henan University Kaiteng 475004 P. R. Ghina

The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking *** at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is *** framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of *** the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation *** that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion *** object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical *** theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： maneuvering target tracking interacting multiple model(IMM) central difference Kalman filter(CDKF) bootstrapping observation

maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

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High Technology Letters 2015年第1期21卷 39-45页

作者：胡振涛 Fu Chunling Cao Zhiwei Li Congcong Instituteof Image Processing and Pattern Recognition Henan University School of Physics and Electronics Henan University

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering *** with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is ***,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation ***,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering *** the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

关键词： maneuvering target tracking nonlinear filtering cubature Kalman filter(CKF) interacting multiple model(IMM)

Amended Kalman Filter for maneuvering target tracking

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Chinese Journal of Electronics 2016年第6期25卷 1166-1171页

作者： YANG Yongjian FAN Xiaoguang ZHUO Zhenfu WANG Shengda NAN Jianguo XU Yunshan Aeronautics and Astronautics Engineering College Air Force Engineering University

The conventional Kalman filter(KF) which uses the current measurement to estimate the current state is a posterior estimation. KF is identified as the optimal estimation in linear models with Gaussian noise. However,the performance of KF with incomplete information may be degraded or diverged. In order to improve the performance of KF, an Amended KF(AKF) is proposed by using more posterior measurements. The principle, derivation and recursive process of AKF are presented. The differences among Kalman smoother, adaptive fading method and AKF are analyzed. The simulation results of target tracking with different covariance of motion model indicate the high precision and robustness of AKF.

关键词： Kalman filter(KF) Incomplete information Amended KF(AKF) maneuvering target tracking

A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

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High Technology Letters 2014年第4期20卷 355-362页

作者：胡振涛 Fu Chunling Institute of Image Processing and Pattern Recognition Henan University School of Physics and Electronics Henan University

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is *** with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new *** the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle *** addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement *** the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor ***,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： multi-sensor information fusion weight optimization correlated noises maneuvering target tracking

maneuvering target tracking using threshold interacting multiple model algorithm

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Journal of Southeast University(English Edition) 2005年第4期21卷 440-444页

作者：徐迈山秀明徐保国清华大学电子工程系北京100084 江南大学通信与控制学院无锡214036

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.

关键词： maneuvering target tracking Kalman filter interacting multiple model (IMM) threshold interacting multiple model (TIMM)

Ant colony optimization for bearings-only maneuvering target tracking in sensors network

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控制理论与应用（英文版） 2007年第3期5卷 301-306页

作者： Benlian XU Zhiquan WANG Zhengyi WU Department of Information and Control Engineering Changshu Institute of Technology Changshu Jiangsu 215500 China School of Automation Nanjing University of Science and Technology Nanjing Jiangsu 210094 China

In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated. Two objectives are proposed and optimized by the ant colony optimization （ACO）, then two kinds of node searching strategies of the ACO algorithm are presented. On the basis of the nodes determined by the ACO algorithm, the interacting multiple models extended Kalman filter （IMMEKF） for the multi-sensor bearings-only maneuvering target tracking is introduced. Simulation results indicate that the proposed ACO algorithm performs better than the Closest Nodes method. Furthermore, the Strategy 2 of the two given strategies is preferred in terms of the requirement of real time.

关键词： Ant colony algorithm Multi-objective optimization maneuvering target tracking Bearings-only

Consistent Extended Kalman Filter Design for maneuvering target tracking and Its Application on Hand Position tracking

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Guidance, Navigation and Control 2022年第2期2卷 33-58页

作者： Lin Tian Yang Xu Wenchao Xue Long Cheng The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China School of Arti-cial Intelligence University of Chinese Academy of SciencesBeijing 100049P.R.China The Key Laboratory of Systems and Control National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190P.R.China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049P.R.China

This paper propees the consistent extended Kalman flter(CEKF)for the maneuvering target tracking(MTT)with nonlinear uncertain dynamics,and applies it on hand poition *** general modlel of the MTT system i presented with unmodleled dynamics in terms of nonlinear unknown function of *** CEKF is propoeed to ensure that the bounds of the estimation error's covariance matrix are av ailable through the flter *** a result,the creponding accuracy of the flter approach can be achieved ***,a CEKF-baaed MTT algorithm is constructed via the tumning aw of the critical parameter matrix QE Finally,the efectiveness of CEKF i verified by MTT numerical simulations and hand tacking expeiments under dilferent ***,two indices are employed to compare the CEKF with extended Kalman filter(EKF):the mean square errors(MSEa)and the bounded percentage,ie the percentage of the rang w bere the estimation error is encboed by the bound calculated by *** MSEs of CEKF are smaller than thoee of EKF,where the worst MSEa of CEKF and EKF are0.14 and 418 in the simulation,a8 well 80.11 and 059 in the expeiments,respectively;all bounded percentages of CEKF are larger than thoee of EKF,where the wonst average bounded percentages of CEKF and EKF ame 87.86%and 14.58%,8 well as 97.41%and 41.79%in the experiments,reapectively.

关键词： maneuvering target tracking hand position tracking extended Kalman¯lter(EKF) consistency

A modified variable rate particle filter for maneuvering target tracking

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Frontiers of Information Technology & Electronic Engineering 2015年第11期16卷 985-994页

作者： Yun-fei GUO Kong-shuai FAN Dong-liang PENG Ji-an LUO Han SHENTU Automation School Hangzhou Dianzi University

To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter （MVRPF） is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.

关键词： maneuvering target tracking Prolonged smooth regions Variable rate model Maneuver detection

A Fuzzy Adaptive Algorithm Based on“Current”Statistical Model for maneuvering target tracking

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Defence Technology（防务技术） 2010年第3期6卷 194-199页

作者：王向华覃征杨慧杰杨新宇 Department of Computer Science and Technology Tsinghua University Department of Computer Science and Technology Xi'an Jiaotong University

The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering *** order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in *** last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.

关键词： control theory maneuvering target tracking "current"statistical model fuzzy control simulation analyses