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

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

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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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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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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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A Fuzzy Adaptive Strong Tracking algorithm with Fading Factor 14

A Fuzzy Adaptive Strong Tracking Algorithm with Fading Facto...

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14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Fang, Shuai Zhao, Chuchu Sun, Jinping CETC Res Inst 3 Beijing Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781665400046

The maximum acceleration parameter determines the effect of the current statistical (CS) model. Thus, when tracking weak maneuvering targets or targets whose actual acceleration exceeds the given value, the tracking performance of the traditional algorithm that sets with a priori fixed value will drop sharply. To solve this problem, a fuzzy adaptive strong tracking algorithm with fading factor (IAFCS-IMM) is proposed. The algorithm adopts a two-level fuzzy logic system. Through the first-level fuzzy logic, a maneuvering factor representing the maneuverability of the target is obtained according to the estimated acceleration information of the model, and the maximum acceleration parameter is adaptively modified. The second-level fuzzy logic is adopted to adjust the model update probability of interacting multiple model (IMM) algorithm according to the maneuver factor. Besides, a fading factor is introduced in the filtering process, which can enhance the robustness of the filter to the sharp mutation of the target state. Simulation results demonstrate that IAFCS-IMM algorithm achieves good results in filtering accuracy and tracking stability of maneuvering targets.

关键词： maneuvering target tracking fuzzy logic CS model interacting multiple model algorithm

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A Modified Tracking algorithm for NSHV Based on Aerodynamic model

A Modified Tracking Algorithm for NSHV Based on Aerodynamic ...

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2021 CIE International Conference on Radar, Radar 2021

作者： Zhang, Jiaqi Zheng, Jibin Sun, Dianxing Liu, Hongwei Xidian University National Laboratory of Rader Signal Processing Xian China China Electronic Technology Group Corporation Science and Technology on Electronic Test and Measurement Laboratory Qingdao China

ISBN: (纸本)9781665498142

In order to track the near space hypersonic vehicle (NSHV), we propose a tracking model containing aerodynamic and kinematic information using interacting multiple model (IMM) algorithm with accruate speed prior information. Because traditional maneuver models are difficult to describe the complex movement characteristics, which involves large-scale high-speed mobile, large flight cover, etc., fault-acceleration model with surface damage model and external disturbance model are proposed. With the model, the state equations are constructed and the system dynamic error is deduced taking variable structure multiple model (VSMM) algorithm as the flitering algorithm. Moreover, an adaptive method for the change of control variables is proposed for setting parameters apporiately in the situations when a prior information is unknown. Simulation experiments are conducted to validate the superiorities among all tracking algorithms in this paper. © 2021 IEEE.

关键词： aerodynamic model interacting multiple model algorithm near space hypersonic vehicle tracking

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Channel tracking of uncoordinated access under uncertain evolution for massive connectivity with massive MIMO

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2021年 141卷

作者： Soltanizadeh, Hediyeh Falahati, Abolfazl Iran Univ Sci & Technol IUST Dept Elect Engn Tehran Iran

Massive MIMO technologies are a major candidate to serve high-rate Internet of Things (IoT) with moving or motionless devices if all channels of devices are known to BS. In this paper, an unknown dynamical model for channel evolution is assumed over time, and regardless of whether devices send pilots or not, joint channel estimation and tracking are investigated. At first, a new suboptimal tracker, Uncertain interacting multiple model (UIMM), is derived for the coordinated access. We define a new setup, referred to as an uncoordinated access, in which many devices with an unknown probabilities access BS without coordination. Then, three novel suboptimal trackers are designed based on UIMM for this uncoordinated access. To decrease computational complexity of the designed trackers, two schemes are applied. The Mean Square Error (MSE) gap between the mentioned scenario and the previous uncoordinated access is computed theoretically and represented by simulation results. Also, a new fundamental limit is defined which is referred to as Cost of Uncoordinated Access under Uncertain Channels (CUAUC). The CUAUC is used to evaluate the MSE gap between the optimal coordinated tracker and the designed suboptimal uncoordinated trackers. Finally, the CUAUC and the efficiency of the designed trackers are evaluated via simulations.

关键词： Uncoordinated access Internet of things Massive MIMO Channel tracking interacting multiple model algorithm Channel aging

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Adaptive Tracking Method of Distorted Voltage Using IMM algorithm under Grid Frequency Fluctuation Conditions

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ENERGIES 2021年第23期14卷 7944-7944页

作者： Nie, Haoyao Nie, Xiaohua Nanchang Univ Sch Econ & Management Nanchang 330031 Jiangxi Peoples R China Univ Manitoba IH Asper Sch Business Winnipeg MB R3T 5V4 Canada Nanchang Univ Dept Energy & Elect Engn Nanchang 330031 Jiangxi Peoples R China

This paper newly proposes an interactive multiple model (IMM) algorithm to adaptively track distorted AC voltage with the grid frequency fluctuation. The usual tracking methods are Kalman filter (KF) algorithm with a fixed frequency and KF algorithm with frequency identifier. The KF algorithm with a fixed frequency has a larger covariance parameter to guarantee the tracking robustness. However, it has a large tracking error. The KF algorithm with frequency identifier overly depends on the accuracy and stability of frequency identifier. The advantage of the proposed method is that it is decoupled from frequency detection and does not depend on frequency detection accuracy. First, the orthogonal vector dynamic (OVD) tracking model of the sine wave is established. Then, a model set covering the grid frequency fluctuation range is formed, and a new OVD-IMM tracking algorithm is proposed in combination with IMM algorithm. In the end, the effectiveness and accuracy of the proposed OVD-IMM algorithm are verified through simulations and experiments.

关键词： frequency fluctuation distorted voltage Kalman filter dynamic tracking model interacting multiple model algorithm

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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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IMM algorithm Based on H_∞ Filter for Maneuvering Target Tracking 36

IMM Algorithm Based on <i>H</i><sub>∞</sub> Filter for Mane...

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36th Chinese Control Conference (CCC)

作者： Liu, Meiqin Fan, Zhen Wang, Xie Zhang, Senlin Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9789881563934

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

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

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