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Dynamic error spectrum for estimation performance evaluation: a case study on interacting multiple model algorithm

IET SIGNAL PROCESSING 2014年第2期8卷 202-210页

作者： Mao, Yanhui Han, Chongzhao Duan, Zhansheng Xi An Jiao Tong Univ Sch Elect & Informat Engn Key Lab Intelligent Networks & Network Secur MOE Minist EducInst Integrated Automat Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ CIESR Xian 710049 Shaanxi Peoples R China

The commonly used root-mean-square error for estimation performance evaluation is easily dominated by large error terms. So many new alternative absolute metrics have been provided in X. R. Li's work. However, each of these metrics only reflects one narrow aspect of estimation performance, respectively. A comprehensive measure, error spectrum, was presented aggregating all these incomprehensive measures. However, when being applied to dynamic systems, this measure will have three dimensions over the total time span, which is not intuitive and difficult to be analysed. To overcome its drawbacks, a new metric, dynamic error spectrum (DES), is proposed in this study to extend the error spectrum measure to dynamic systems. Three forms under different application backgrounds are given, one of which is balanced taking into account both good and bad behaviour of an estimator and so can provide more impartial evaluation results. It can be applied to a variety of dynamic systems directly. Then the challenge in performance evaluation of the interacting multiple model (IMM) algorithm is considered, and the IMM algorithm is chosen as the testing case to illustrate the superiority of the DES metric. The simulation results validate its utility and effectiveness.

关键词： estimation theory mean square error methods dynamic error spectrum estimation performance multiple model algorithm root-mean-square error performance evaluation dynamic systems interacting multiple model algorithm IMM algorithm DES metric

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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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Robust MT Tracking Based on M-Estimation and interacting multiple model algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第7期59卷 3398-3409页

作者： Hammes, Ulrich Zoubir, Abdelhak M. Tech Univ Darmstadt Inst Telecommun Signal Proc Grp D-64283 Darmstadt Germany

An algorithm for mobile terminal (MT) tracking based on time-of-arrival measurements in non-line-of-sight (NLOS) environments where NLOS measurements are modeled as positive outliers is proposed. Standard filters such as the extended Kalman filter (EKF) fail because they are sensitive to outliers. In contrast, a robust EKF (REKF) always trades off efficiency in line-of-sight (LOS) versus robustness in NLOS environments and it is not possible to achieve both with the same filter. Instead, we propose to use two filters in parallel in a multiple model framework. An EKF yields high precision in LOS environments whereas an REKF provides robust state estimates when NLOS propagation comes into play. The state estimates of either filters are combined automatically based on the confidence we have for the underlying situation. It is shown via numerical studies that the proposed algorithm yields positioning accuracy similar to the EKF in LOS environments and even significantly outperforms the REKF in NLOS environments.

关键词： interacting multiple model algorithm M-estimation mobile terminal tracking NLOS mitigation

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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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A Reduced-Complexity interacting multiple model algorithm for Location Tracking in Heterogeneous Observation

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2015年第3期2卷 173-176页

作者： Fu, Xiaoyan Shang, Yuanyuan Capital Normal Univ Beijing Adv Innovat Ctr Imaging Technol Coll Informat Engn Beijing 100191 Peoples R China Capital Normal Univ Beijing Key Lab Elect Syst Reliabil Technol Beijing 100191 Peoples R China

This paper is devoted to the problem of state estimate of discrete-time stochastic systems. A low-complexity and high accuracy algorithm is presented to reduce the computational load of the traditional interacting multiple model algorithm with heterogeneous observations for location tracking. By decoupling the x and y dimensions to simplify the implementation of location, updated information is iteratively passed based on an adaptive fusion decision. Simulations show that the algorithm is more computationally attractive than existing multiple model methods.

关键词： data fusion interacting multiple model algorithm location tracking wireless sensor networks

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Maneuvering target tracking based on a random motion model and integrated random interacting multiple model filtering

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AEROSPACE SCIENCE AND TECHNOLOGY 2025年 162卷

作者： Yang, Fengqi Zhong, Jinshan Luo, Yingting Zhang, Ying Shen, Xiaojing Zhu, Yunmin Sichuan Univ Coll Math Chengdu 610064 Sichuan Peoples R China

Traditional maneuvering target tracking algorithms assume that the target motion model is either fixed or limited in number. For high-speed and highly maneuvering targets, the tracker's performance degrades rapidly when the model set fails to adequately encompass the maneuvering mode or when there is a substantial deviation. Therefore, we propose a novel maneuvering target tracking method based on a random motion model. This algorithm employs a random model to describe the target maneuver, which is more widely applicable than traditional algorithms and remains more stable when the target maneuver is not covered by the model set. Additionally, in cases where the model set of the interacting multiple model algorithm (IMM) does not align with the actual maneuvering state, the new method exhibits a smaller tracking error compared to IMM and shows no divergence trend. Finally, we combine IMM and the random motion model to propose an Integrated Random interacting multiple model algorithm (IRIMM). The performance of the IRIMM algorithm closely matches that of IMM when provided with a perfectly accurate model set and significantly improves tracking effectiveness and stability when the model is incorrect.

关键词： Random motion model Maneuvering target tracking interacting multiple model algorithm Integrated random interacting multiple model algorithm

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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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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 Multi-model Fault Diagnosis of Dynamic Systems for Motion Tracking

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IEEE ACCESS 2024年 12卷 197540-197556页

作者： Daniels, Annalena Benciolini, Tommaso Wollherr, Dirk Leibold, Marion Tech Univ Munich Chair Automat Control Engn D-80333 Munich Germany

For most real-world systems, the exact description of possible faults is unknown, making these faults difficult to detect, and even more difficult to identify. The most promising way is to use multiple hypotheses for faults to find the best fitting fault model by comparing system measurements with the predictions of the multi-model algorithm. However, this may lead to the need for infinite hypotheses. We propose a novel multi-model approach that considers a small number of different models with a known macro-structure and unknown parameters, combining system identification with simultaneous fault diagnosis. The unknown parameters in the models are estimated using a maximum likelihood approach. The fitted models are then used in an interacting multiple model algorithm to determine the most likely model that best describes the system behavior at any moment in time. An overfitting problem emerging from short data sequences is discussed, and two solutions are introduced. First, a regularization term in the probability estimation is suggested to penalize frequent parameter changes that signal possible overfitting. Second, an algorithm with a shifted data set is presented. The effectiveness of the algorithms is demonstrated on a motion tracking problem where the different fault hypotheses represent the macro-behavior of a moving object, and the real system switches between different modes. In a comparison, the proposed algorithms are the only ones that reliably identify the defined faults. They can be easily adapted to other fault diagnosis problems.

关键词： Adaptation models Maximum likelihood estimation Computational modeling Vectors Fault detection Switches Dynamical systems Adaptive systems System identification Robot sensing systems Parameter estimation fault diagnosis multiple model algorithm maximum likelihood estimation interacting multiple model algorithm

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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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