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第36届中国控制会议

作者： Meiqin Liu Zhen Fan Xie Wang Senlin Zhang State Key Laboratory of Industrial Control Technology Zhejiang University College of Electrical Engineering Zhejiang University

Correct knowledge of noise statistics is essential for an effective estimator in maneuvering target *** practice,however,the noise statistics are usually unknown or not perfectly *** deal with the estimation problem in linear discretetime systems with Markov jump parameters,where the measurement noise covariance is unknown,a novel approach is presented in this *** approach is based on the interacting multiple model(IMM) *** H Alter is employed to construct a noise statistics estimator to obtain the information which is necessary for the IMM *** our approach,even the priori knowledge of noise statistics is not *** noise statistics loss problem is solved while the merits of IMM algorithm is *** effectiveness of the proposed approach is demonstrated in comparison with single-model H Alter through Monte Carlo simulation for maneuvering target tracking.

关键词： State Estimation Noise Statistics Estimate H∞ Filter interacting multiple model algorithm Target Tracking

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The two interacting multiple model algorithms with removing error measurement 11

The two interacting multiple model algorithms with removing ...

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2014 11th World Congress on Intelligent Control and Automation, WCICA 2014

作者： Wang, Wei Li, Dan School of Science Naval Univ. of Technology Wuhan China

In the centralized multi-sensor system, there is often a problem that how to remove measurements used early from the current state estimation, then re-calculate the current state estimation. The interacting multiple model (IMM) algorithm with removing old measurement presented by Bar-shalom is based on the standard IMM algorithm and Bl1 algorithm. However, in the standard IMM algorithm, the combination of normal probability density function and probability mass function results in that updated model weights are only an approximate probability. Moreover, sometimes the use of equivalent measurement can cause the performance of algorithm to deteriorate and the problem of Rank Deficiency. In order to deal with these drawbacks, based on the optimal fusion rule weighted by scales, the optimal fusion rule weighted by diagonal matrix and the globally optimal forward-prediction filtering algorithm with removing old measurement, a novel IMM algorithm is presented. The novel algorithm has not those drawbacks mentioned above. Simulation shows the feasibility and superiority of the new algorithm. © 2014 IEEE.

关键词： Error measurement Information fusion interacting multiple model algorithm Optimal fusion rule weighted by diagonal matrix Optimal fusion rule weighted by scales

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Anti-ship Missile Trajectory Tracking Based on CS_UKF And interacting multiple models 33

Anti-ship Missile Trajectory Tracking Based on CS_UKF And In...

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33rd Chinese Control Conference (CCC)

作者： Xu Tian Ye Xu Xue Song East China Jiaotong Univ Elect Coll Nanchang 330013 Peoples R China

ISBN: (纸本)9781479947249

Anti-ship missile is a kind of typical high maneuvering target which has the characteristics of strong motor and multiple mobile. Especially its unique form of "snake maneuver" brought great difficulties to the tracking system. This article used the IMM algorithm and the "current" statistical model constituted model set, together with the method of UKF filter which avoid extended kalman tedious Jacobi matrix calculation, then we get a new method for anti-ship missiles tracking. Results show that the method has good tracking performance to strong maneuvering target just like anti-ship missile.

关键词： Anti-ship missile tracking Current statistic model UKF interacting multiple model algorithm

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An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

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DIGITAL SIGNAL PROCESSING 2013年第5期23卷 1678-1685页

作者： Amirzadeh, Ahmadreza Karimpour, Ali Ferdowsi Univ Mashhad Fac Engn Mashhad Iran

In this paper, the interaction and combination of Fuzzy Fading Memory (FFM) technique and Augmented Kalman Filtering (AUKF) method are presented for the state estimation of non-linear dynamic systems in presence of maneuver. It is shown that the AUKF method in conjunction with the FFM technique (FFM-AUKF) can estimate the target states appropriately since the FFM tunes the covariance matrix of the AUKF method in presence of unknown target accelerations by using a fuzzy system. In addition, the benefits of both FFM technique and AUKF method are employed in the scheme of well-known interacting multiple model (IMM) algorithm. The proposed Fuzzy IMM (FIMM) algorithm does not need the predefinition and adjustment of sub-filters with respect to the target maneuver and reduces the number of required sub-filters to cover the wide range of unknown target accelerations. The Monte Carlo simulation analysis shows the effectiveness of the above-mentioned methods in maneuvering target tracking. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Maneuvering target tracking Augmented Kalman Filtering method Fuzzy Fading Memory technique interacting multiple model algorithm

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Adaptive Variable Structure multiple model algorithm base on High Maneuvering Target Tracking

Adaptive Variable Structure Multiple Model Algorithm base on...

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第23届过程控制会议

作者： Zeng Dong Huang Xiao-peng Peng Dong-liang Institution of Information and Control Key National Defense Laboratory of Fundamental Science for I Institution of Information and Control Key National Defense Laboratory of Fundamental Science for I

The traditional interacting multiple model (IMM) algorithm has a fixed structure, in the region of high maneuvering target tracking, needs plenty of models to describe the target maneuver, brings the contradiction of tracking performance and calculating amount, meanwhile, unnecessary inter-competition among the models maybe reduce the performance. To resolve this problem caused by IMM, an adaptive variable structure multiple model (AVSMM) algorithm is presented. According to the current target maneuvering level, just several models which related to the target motion, at different moments, the algorithm can adjust the model’s parameters and get a new model set, then begin to filter estimate. Simulation results show that this algorithm can match actual target trajectory with less computational complexity and better accuracy.

关键词： Adaptive Variable-structure multiple model interacting multiple model algorithm High Maneuvering target tracking

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PCRLB-based Optimal Sensor Selection for Maneuvering Target Tracking

PCRLB-based Optimal Sensor Selection for Maneuvering Target ...

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7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM)

作者： Liu, Zhigang Wang, Jinkuan Northeastern Univ Inst Engn Optimizat & Smart Antenna Qinhuangdao 066004 Peoples R China

ISBN: (纸本)9781424462520

To deal with the problem of tracking a maneuvering target in range-based sensor networks, we firstly derive the multiple model posterior Cramer-Rao lower bound(PCRLB), and on the basis of this bound, choose the sensor subset that may attend the incoming tracking event. Secondly, we design the sensor selection strategy under communication constraint, and further pick up the optimal sensor. Thirdly, we can estimate the state of the maneuvering target by making use of the interacting multiple model(IMM) algorithm. Finally, the simulation results show the effectiveness of the proposed scheme.

关键词： Covariance matrix Equations IMM algorithm Mathematical model PCRLB-based optimal sensor selection Prediction algorithms Radar tracking Signal processing Target tracking interacting multiple model algorithm maneuvering target tracking multiple model posterior Cramer-Rao lower bound optimal sensor range-based sensor networks sensor placement sensor placement target tracking wireless sensor networks wireless sensor networks

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Cluster-based centralized data fusion for tracking maneuvering targets using interacting multiple model algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2004年第2期29卷 205-216页

作者： Vaidehi, V Kalavidya, K Gandhi, SI Anna Univ Madras Inst Technol Dept Elect Engn Madras 600044 Tamil Nadu India

The interacting multiple model (IMM) algorithm has proved to be useful in tracking maneuvering targets. Tracking accuracy can be further improved using data fusion. Tracking of multiple targets using multiple sensors and fusing them at a central site using centralized architecture involves communication of large volumes of measurements to a common site. This results in heavy processing requirement at the central site. Moreover, track updates have to be obtained in the fusion centre before the next measurement arrives. For solving this computational complexity, a cluster-based parallel processing solution is presented in this paper. In this scheme, measurements are sent to the data fusion centre where the measurements are partitioned and given to the slave processors in the cluster. The slave processors use the IMM algorithm to get accurate updates of the tracks. The master processor collects the updated tracks and performs data fusion using 'weight decision approach'. The improvement in the computation time using clusters in the data fusion centre is presented in this paper.

关键词： interacting multiple model algorithm data fusion target tracking cluster-based parallel processing solution

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IMM algorithm using intelligent input estimation for maneuvering target tracking

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2005年第5期E88A卷 1320-1327页

作者： Lee, BJ Park, JB Joo, YH ROK Navy Chinhae Kyungnam South Korea Naval Combat Syst Div Agcy Def Dev Chinhae Kyungnam South Korea Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea Kunsan Univ Sch Elect & Informat Engn Kunsan South Korea

A new interacting multiple model (IMM) algorithm using intelligent input estimation (IIE) is proposed for maneuvering target tracking. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown target acceleration by a fuzzy system using the relation between the residuals of the maneuvering filter and the non-maneuvering filter. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of target acceleration. Then, multiple models are represented as the acceleration levels estimated by these fuzzy systems, which are optimized for different ranges of target acceleration. In computer simulation for an incoming anti-ship missile, it is shown that the proposed method has better tracking performance compared with the adaptive interacting multiple model (AIMM) algorithm.

关键词： interacting multiple model algorithm intelligent input estimation maneuvering target fuzzy system genetic algorithm

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A distributed multirate IMM algorithm for multiplatform tracking

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MATHEMATICAL AND COMPUTER modelLING 2000年第10期32卷 1095-1116页

作者： Hong, L Ding, Z Wright State Univ Dept Elect Engn Dayton OH 45435 USA

A distributed multirate interacting multiple model (DMRIMM) algorithm for multi-platform tracking is proposed. Under the assumption that each model operates at an update rate proportional to the model's assumed dynamics, a wavelet transform is used to generate equivalent lent multirate measurements and a multirate constant-velocity (MRCV) model is derived. Thus, a multirate interacting multiple model (MRIMM) algorithm using an MRCV model and multirate measurements is obtained. Based on the multirate IMM, its distributed version DMRIMM algorithm for multiplatform tracking is proposed. The MRIMM algorithm is first employed to perform each local/platform estimation. A global filter is then constructed to perform a fusion of MRIMM estimations. The advantages of low computation loads and performance improvement are demonstrated through Monte Carlo simulations. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： interacting multiple model algorithm multiplatform tracking distributed system multirate measurement multirate model

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An interacting multiple model algorithm with a switching Markov chain

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MATHEMATICAL AND COMPUTER modelLING 1997年第1期25卷 1-9页

作者： Ding, Z Hong, L Department of Electrical Engineering Wright State University Dayton OH 45435 U.S.A.

A Markov chain plays an important role in an interacting multiple model (IMM) algorithm which has been shown to be effective for target tracking systems. Such systems are described by a mixing of continuous states and discrete modes. The switching between system modes is governed by a Markov chain. In real world applications, this Markov chain may change or needs to be changed. Therefore, one may be concerned about a target tracking algorithm with the switching of a Markov chain. This paper concentrates on fault-tolerant algorithm design and algorithm analysis of IMM estimation with the switching of a Markov chain. Monte Carlo simulations are carried out and several conclusions are given.

关键词： interacting multiple model algorithm fault-tolerant target tracking model selection/design switching of a Markov chain

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