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22nd International Conference on Electrical Machines and Systems (ICEMS)

作者： Wang, Shuai Guan, Xiaocun Guo, Denghua Guan, Shaohua Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Powe Wuhan Peoples R China

ISBN: (纸本)9781728133980

The electromagnetic coil launcher is a special linear motor in which the position detection of the armature plays a crucial role in ensuring the reasonable triggering of each coil and analyzing the performance of the system. At present, there are inevitably some errors in common position detection methods, and the motion model of the armature has the characteristics of nonlinear and strong coupling, in this case, in order to obtain the position and speed of the armature at the same time as the error is as small as possible, an estimation algorithm based on cubature Kalman filter(CKF) and interactive multiple model(IMM) is proposed in this paper. This algorithm adopts three known motion models to replace the complex motion model of the armature, then the observation position of the armature is filtered by CKF. Finally, the IMM-CKF algorithm is verified by the actual measured data during the launch process, and compares with CKF and adaptive iterative CKF with forgetting factor. The results show that the accuracy and stability of IMM-CKF are significantly better than those of the latter two algorithms.

关键词： electromagnetic coil launcher position detection interactive multiple model Cubature Kalman filter

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interactive multiple model Filter for Tracking a Pursuer With Proportional Navigation Guidance Law

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2018年第8期140卷 081003-081003页

作者： Du, Runle Zou, Xinguang Zhou, Di Liu, Jiaqi Natl Key Lab Sci & Technol Test Phys & Numer Math Beijing 100076 Peoples R China Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Heilongjiang Peoples R China Harbin Inst Technol Sch Astronaut Harbin 150001 Heilongjiang Peoples R China

This paper addresses a pursuer tracking problem where the pursuer's acceleration is given by a proportional navigation (PN) guidance law with a time-varying navigation ratio which varies with the relative range between the pursuer and its target. Based on a motion model that exactly describes the relative motion and the PN guidance law, a novel filter for tracking such a pursuer is designed using interactive multiple model (IMM) algorithm and unscented Kalman filtering (UKF) technique. This filter is able to accurately estimate the relative range, relative velocity, and the acceleration of pursuer even if the pursuer adopts a PN guidance law with time-varying navigation ratio. The proposed tracking method is evaluated in extensive Monte Carlo simulations. It is shown that accurate estimation results have been obtained, and the model probabilities in the IMM UKF filter are consistent with real situations.

关键词： purser tracking proportional navigation law interactive multiple model unscented Kalman filtering

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Aircraft Guidance Law Identification Using interactive multiple model Estimation

Aircraft Guidance Law Identification Using Interactive Multi...

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第30届中国控制与决策会议

作者： Sai Li Huajing Fang School of Automation National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology

The avoidance of guided missiles is a very important factor for survivability in modern air combat. A guidance law identification filter, based on the interactive multiple model using extended kalman filter（IMM-EKF）has been proposed in this paper. In the guidance law identification filter, the filter bank was composed of relative motion models based on true proportional navigation and deviated pursuit navigation guidance law. The measurements including the relative distance, line-to-sight angle and acceleration of unknown aircraft were used for estimation. The proposed algorithm was able to identify the type of deployed guidance law by comparing model probabilities of each filter. The numerical simulation of various engagement scenarios demonstrated the satisfactory performance of IMM-EKF model for guidance law identification.

关键词： Guidance Law Identification interactive multiple model Extended Kalman Filter

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Robust visual tracking based on interactive multiple model particle filter by integrating multiple cues

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NEUROCOMPUTING 2014年 135卷 118-129页

作者： Dou, Jianfang Li, Jianxun Shanghai Jiao Tong Univ Minist Educ China Key Lab Syst Control & Informat Proc Dept Automat Shanghai 200240 Peoples R China

Visual tracking can be formulated as a state estimation problem of target representation based on observations in image sequences. To investigate the integration of rough models from multiple cue and to explore computationally efficient algorithms, this paper formulates the problem of multiple cue integration and tracking to combine interactive multiple model (IMM) with particle filter (IMM_PF). interactive multiple model can estimate the multiple cue state of a dynamic system with several behavioral models that switch from one to another using model likelihoods and model transition probabilities. For the problem of visual tracking, the model of IMM is adopted to three target observation models: Corrected Background Weighted Histogram (CBWH), Completed Local Ternary Patterns (CLTP) and Histogram of Oriented Gradients (HOG). The probabilities of these models are corresponding to the weights of multiple cues. IMM_PF then dynamically adjusts the weights of different features. Compared with those state-of-the-art methods in the tracking literature, this algorithm can track the object accurately in conditions of rotation, abrupt shifts, as well as clutter and partial occlusions occurring to the tracking object with good robustness, as demonstrated by experimental results. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Visual tracking Particle filter interactive multiple model Corrected background weighted histogram Completed local ternary patterns Histogram of oriented gradients

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Robust Tracking Method for Small and Weak multiple Targets Under Dynamic Interference Based on Q-IMM-MHT

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SENSORS 2025年第4期25卷 1058-1058页

作者： Yang, Ziqian Nie, Hongbin Liu, Yuxuan Bian, Chunjiang Chinese Acad Sci Natl Space Sci Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In complex environments, traditional multi-target tracking methods often encounter challenges such as strong clutter interference and interruptions in target trajectories, which can result in insufficient tracking accuracy and robustness. To address these issues, this paper presents an improved multi-target tracking algorithm, termed Q-IMM-MHT. This method integrates multiple Hypothesis Tracking (MHT) with interactive multiple model (IMM) and introduces a Q-learning-based adaptive model switching strategy to dynamically adjust model selection in response to variations in the target's motion patterns. Furthermore, the algorithm utilizes Support Vector Machines (SVMs) for anomaly detection and trajectory recovery, thereby enhancing the accuracy of data association and the overall robustness of the system. Experimental results indicate that under high noise conditions, the Root Mean Square Error (RMSE) of position estimation decreases to 0.74 pixels, while the RMSE of velocity estimation falls to 0.04 pixels/frame. Compared to traditional methods such as the Unscented Kalman Filter (UKF), IMM, and CIMM, the RMSE is reduced by at least 10.84% and 42.86%, respectively. In scenarios characterized by target trajectory interruptions and clutter interference, the algorithm maintains an association accuracy exceeding 46.3% even after 30 frames of interruption, significantly outperforming other methods. These findings demonstrate that the Q-IMM-MHT algorithm offers substantial performance improvements in multi-target tracking tasks within complex environments, effectively enhancing both tracking accuracy and stability, with considerable application value and extensive potential for future use.

关键词： multi-target tracking point target multiple hypothesis tracking interactive multiple model adaptive model switching

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Improved interactive multiple models based on Self-adaptive Turn model for Maneuvering Target Tracking 8

Improved Interactive Multiple Models based on Self-adaptive ...

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8th International Conference on Information Science and Technology (ICIST)

作者： Zhou, Rong Zhou, Kemin Wu, Menghua Teng, Jing North China Elect Power Univ Sch Control & Comp Engn Beijing 102200 Peoples R China

ISBN: (纸本)9781538637821

Tracking maneuvering target is a challenging problem and interactive multiple model (IMM) is proved an effective solution for it. In multiple model, the constant turn model (CT) is usually used to describe the target's turning motion. However, fixed or partially adaptive turn angular rate omega is usually adopted in CT which leads to tracking accuracy decrease. In this paper, an improved interactive multiple model set based on self-adaptive CT model is proposed. In self-adaptive CT model, the value of the turn angular rate omega is calculated based on both x and y velocity instead of only one of them or fixed value. To verify the improvement, particle filter, which is proved an effective way to solve non Gaussian nonlinear problem, is used to track maneuvering target. The performance of the proposed multiple model set is verified in two different scenarios and compared to two widely used multiple model sets. Simulation results show that the proposed model set has better performance both in tracking accuracy and computational cost.

关键词： interactive multiple model constant turn model particle filter

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Research on interactive multi-model fault diagnosis method of Li-ion battery based on noise suppression

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第9期45卷 13198-13218页

作者： Wang, Yongchao Meng, Dawei Li, Ran Zhou, Yongqin Zhang, Xiaoyu Harbin Univ Sci & Technol Sch Elect & Elect Engn Harbin 150080 Heilongjiang Peoples R China Harbin Univ Sci & Technol Minist Educ Engn Res Ctr Automot Elect Drive Control & S Harbin Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China

The multi-mode-adaptive estimation (MMAE) algorithm lacks the noise suppression capability as impacted by the isolation of each model, which reduces the accuracy of fault diagnosis and causes false alarms. Accordingly, an interactive multiple model (IMM) algorithm was proposed in the present study combined with unscented Kalman filter (UKF) to diagnose multiple faults of lithium-ion batteries. The IMM algorithm consisted of the Markov transition probability matrix (TPM), which enabled the real-time interaction of information at the input of each model. Moreover, the output end fed the updated probability information of the respective model to the input end of the filter by complying with the TPM, which reduced the effect of noise on the algorithm. As demonstrated by the experimental research, the IMM-UKF algorithm is capable of reducing the error attributed to noise, increasing the accuracy of lithium-ion battery fault diagnosis, lowering the fault false alarm rate, and accurately detecting battery fault information.

关键词： multi‐ fault diagnosis of battery noise suppression Markov transition probability matrix interactive multiple model unscented Kalman filter

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Non-Conservative Trajectory Planning for Automated Vehicles by Estimating Intentions of Dynamic Obstacles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2463-2481页

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

Motion planning algorithms for urban automated driving must handle uncertainty due to unknown intention and future motion of Dynamic Obstacles (DOs). Considering a single future trajectory for each DO is not adequate, especially in urban frameworks where traffic participants exhibit very different behaviors. However, including multiple candidate trajectories representing different behaviors results in an excess of conservatism. We present a novel combination of the interactive multiple model (IMM) algorithm and Stochastic model Predictive Control (SMPC) that allows non-conservative safe motion planning in presence of DOs with unknown intention. We introduce a framework based on LQR and IMM to predict multiple candidate future trajectories, each interpreted as a high-level intention that the DO is pursuing, and dynamically estimate their probabilities. Then, the future trajectory of the automated vehicle is iteratively planned in an SMPC fashion, in which collision avoidance constraints are generated for multiple future trajectories of each DO, with a focus on the most likely. Our method improves safe motion planning fully exploiting the benefits of multi-modal predictions of the DOs, avoiding excessive conservatism. Advantages of the proposed method are discussed through simulations in the CARLA environment.

关键词： Trajectory Uncertainty Predictive models Planning Safety Heuristic algorithms Prediction algorithms Automated driving interactive multiple model model predictive control safety constraints stochastic model predictive control trajectory planning urban driving

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Fault-Tolerant Cooperative Positioning Based on Hybrid Robust Gaussian Belief Propagation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第6期24卷 6425-6431页

作者： Xiong, Jun Xiong, Zhi Zhuang, Yuan Cheong, Joon Wayn Dempster, Andrew G. Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Jiangsu Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Univ New South Wales Sch Elect Elect & Telecommun Engn Sydney NSW 2052 Australia

This paper proposes a hybrid robust Gaussian belief propagation (HRGBP) as a fault-tolerant cooperative positioning (CP) system that can be used to support cooperative intelligent transportation applications. For fault-tolerant state estimation, it is well known that fault detection and exclusion (FDE) based methods and Huber's M-estimation based methods have their own drawbacks when facing different forms of observation outliers, or faults. To solve this problem, our proposed HRGBP uses an interactive multiple model (IMM) framework to fuse these two strategies, which combines the advantages of both methods without their drawbacks. HRGBP can fully exploit the message passing process to mitigate the biased estimates, which further improves the system's fault-tolerant robustness. HRGBP can be further adapted to fuse more fault-tolerant strategies to improve the robustness of the CP system, and be extended to other factor graph-based methods. Here, we evaluate HRGBP for observations from visual landmark range and bearing, neighboring vehicle range and bearing, and odometer. Our evaluations show that HRGBP outperforms other state-of-the-art CP methods.

关键词： Sensors Fault tolerant systems Fault tolerance Visualization Belief propagation Vehicular ad hoc networks Sensor systems Cooperative positioning Gaussian belief propagation interactive multiple model fault-tolerant positioning

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Dynamic Interference Source Tracking Based on Motion State Decomposition 20

Dynamic Interference Source Tracking Based on Motion State D...

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20th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Zhang, Shijiao Zhang, Guomei Shi, Yezhao Lu, Lei Yin, Jiaoxian Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Shaanxi Smart Networks & Ubiquitous Access Res Ct Xian 710049 Peoples R China Natl Simulat Educ Ctr Commun & Informat Syst Xian 710049 Peoples R China

ISBN: (纸本)9798350361261;9798350361278

Radio interference sources mounted on high-speed mobile platforms present a considerable challenge to accurate localization and tracking due to their wide-ranging and dynamic characteristics. This article tackles the intricate problem of tracking erratically maneuvering interference sources and introduces a dynamic tracking scheme for the spatial decomposition of motion states. Firstly, the proposed scheme decomposes the complex three-dimensional motion into three independent one-dimensional linear movements. This facilitates the concurrent application of interacting multiple model matching filters across all three dimensions, enabling precise characterization of the complex motion states. Secondly, an enhancement is made to the update process of the noise covariance matrix within the real-time noise estimator. Specifically, a time-varying exponential forgetting factor is incorporated to ensure the robustness of the adaptive cubature Kalman filter algorithm when combined with the noise estimator. Simulation results show that the proposed tracking scheme outperforms traditional methods in terms of both the speed of tracking error convergence and the fluctuations reduction. The improved accuracy and reliability of the proposed scheme position it as a promising approach for addressing the challenges associated with tracking dynamic radio interference sources on high-speed mobile platforms.

关键词： Interference Source Target Tracking interactive multiple model Volume Kalman filtering

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