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30th National Conference on Communications (NCC)

作者： Kowshik, Anagha K. Vetrivel, Deepan Thiagarajan, Ganesan Gurugopinath, Sanjeev MMRFIC Technol Private Ltd Bengaluru 560048 India

ISBN: (纸本)9798350359220

In this paper, we present an interactive multiple model (IMM) framework for high-resolution target tracking in millimeter-wave radars, using polar coordinates. Commonly, the motion models are formulated in Cartesian coordinates. However, the radar measurements originate in sensor-specific coordinates, which are often in polar form. We derive the Kalman prediction and update equations in polar coordinates for two motion models, viz., the constant velocity (CV) and the constant acceleration (CA) models. Further, we consider a combination of CV and CA models and derive the updates on the Markovian state transition probabilities for IMM. We derive state estimates using the IMM and quantify its computational complexity. We validate the proposed analysis using experiments based on Monte Carlo simulations and real-world radar data, in terms of target trajectories and root mean squared error.

关键词： Constant feature models interactive multiple model Kalman filters millimeter wave radars polar coordinates

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Robust interactive multiple model for Collaborative Navigation of Unmanned Surface Vessel Swarm 36

Robust Interactive Multiple Model for Collaborative Navigati...

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36th Chinese Control and Decision Conference (CCDC)

作者： Sun, Jingzhou Yin, Haifeng Gao, Bingbing Northwestern Polytech Univ Res & Dev Inst Shenzhen Peoples R China Northwestern Polytech Univ Sch Automat Xian Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This paper presents a robust interactive multiple model (IMM) method to deal with the data fusion of collaborative navigation for the unmanned surface vessel (USV) swarm. Firstly, the IMM is combined with the cubature Kalman filter to handle the nonlinear characteristics involved in the collaborative navigation model based on the strategy of measuring the relative distance and azimuth. Then, a sub-filter in the IMM framework is designed as a robust filter where the Mahalanobis distance criterion is used to detect the abnormal noise statistics. Subsequently, a model selection mechanism is established to adaptively chose the proper models to cover the actual measurement noise statistics. The proposed robust IMM method has a strong adaptability and robustness to handle the varying the measurement noise statistics, it provides an effective collaborative navigation way to enhance the performance of the USV swarm. The simulation on collaborative navigation of USV swarm has been conducted to evaluate the efficacy of proposed robust IMM. The results demonstrate that it has excellent robustness compared to the traditional CKF and CKF-IMM.

关键词： Collaborative navigation interactive multiple model USV swarm Cubature Kalman filter Adaptive model selection

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A Survey of Maneuvering Target Prediction via interactive multiple model 34

A Survey of Maneuvering Target Prediction via Interactive Mu...

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34th Chinese Control and Decision Conference (CCDC)

作者： Na, Wang Kun, Peng Yang, Li Tiangong Univ Sch Control Sci & Engn Tianjin 300387 Peoples R China Key Lab Intelligent Control Elect Equipment Tianjin 300387 Peoples R China

ISBN: (纸本)9781665478960

Maneuvering target prediction is a kind of methods to estimate the future state of moving target with randomness and complexity, according to the information of historical moment or current moment. During the target maneuvering, larger prediction error will be produced by single motion model. So the interactive multiple model is adapted. By this means, several models are used to match with their moving states. Then the parallel filtering is realized, and the results of filters are weighted and combined to obtain the final result. So the prediction accuracy of the proposed method for the more complex motions is higher. In this paper, considering the common states of constant velocity, constant acceleration and variable acceleration, the principle of interactive multiple model is introduced. And then its study and application in target prediction is reviewed. Finally, the development and the trends of the interactive multiple model in the target prediction is analyzed and prospected.

关键词： Maneuvering Target Prediction Target State Estimation Motion model model Predictionor interactive multiple model

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An interactive multiple model method to identify the in-vessel phenomenon of a nuclear plant during a severe accident from the outer wall temperature of the reactor vessel

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NUCLEAR ENGINEERING AND TECHNOLOGY 2021年第2期53卷 532-548页

作者： Khambampati, Anil Kumar Kim, Kyung Youn Hur, Seop Kim, Sung Joong Kim, Jung Taek Jeju Natl Univ Dept Elect Engn Seoul South Korea Korea Atom Energy Res Inst Res Div Autonomous Control Seoul South Korea Hanyang Univ Dept Nucl Engn Seoul South Korea

Nuclear power plants contain several monitoring systems that can identify the in-vessel phenomena of a severe accident (SA). Though a lot of analysis and research is carried out on SA, right from the development of the nuclear industry, not all the possible circumstances are taken into consideration. Therefore, to improve the efficacy of the safety of nuclear power plants, additional analytical studies are needed that can directly monitor severe accident phenomena. This paper presents an interacting multiple model (IMM) based fault detection and diagnosis (FDD) approach for the identification of in-vessel phenomena to provide the accident propagation information using reactor vessel (RV) out-wall temperature distribution during severe accidents in a nuclear power plant. The estimation of wall temperature is treated as a state estimation problem where the time-varying wall temperature is estimated using IMM employing three multiple models for temperature evolution. From the estimated RV out-wall temperature and rate of temperature, the in-vessel phenomena are identified such as core meltdown, corium relocation, reactor vessel damage, reflooding, etc. We tested the proposed method with five different types of SA scenarios and the results show that the proposed method has estimated the outer wall temperature with good accuracy. (C) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC.

关键词： interactive multiple model Fault detection State estimation Identification of in-vessel phenomena RV out-wall temperature

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Improved Adaptive Grid interactive multiple model Algorithm based on Maneuverability Division 5

Improved Adaptive Grid Interactive Multiple Model Algorithm ...

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5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Wang, Weijie Wang, Shengli Zhang, Zhi Nanjing Research Institute of Electronic Technology Nanjing China

The grid points of the Adaptive Grid interactive multiple model (AGIMM) algorithm change slowly at the target high maneuvering and fluctuate when the target is moving at a constant speed. The paper proposes an improved AGIMM algorithm based on maneuverability of target. Firstly, divide the Maneuverability zones by Filter residuals. Secondly, according to different zones, the grid points are updated in different ways to speed up the convergence of the model when the target is high maneuvering, and reduce the fluctuation of the model when the target is low maneuvering. Realize the model update method according to the target maneuverability adaptive adjustment. The simulation shows that the improved algorithm significantly reduces the tracking error regardless of whether the target turns or not. © 2021 IEEE.

关键词： Adaptive Grid Cooperative Turning interactive multiple model Maneuvering tracking

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Adaptive estimations of tyre-road friction coefficient and body's sideslip angle based on strong tracking and interactive multiple model theories

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第14期234卷 3224-3238页

作者： Ping, Xianyao Cheng, Shuo Yue, Wei Du, Yongchang Wang, Xiangyu Li, Liang Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Beijing New Energy Vehicle Co Ltd Beijing Peoples R China

Vehicle dynamic states and parameters, such as the tyre-road friction coefficient and body's sideslip angle especially, are crucial for vehicle dynamics control with close-loop feedback laws. Autonomous vehicles also have strict demands on real-time knowledge of those information to make reliable decisions. With consideration of the cost saving, some estimation methods employing high-resolution vision and position devices are not for the production vehicles. Meanwhile, the bad adaptability of traditional Kalman filters to variable system structure restricts their practical applications. This paper introduces a cost-efficient estimation scheme using on-board sensors. Improved Strong Tracking Unscented Kalman filter is constructed to estimate the friction coefficient with fast convergence rate on time-variant road surfaces. On the basis of previous step, an estimator based on interactive multiple model is built to tolerant biased noise covariance matrices and observe body's sideslip angle. After the vehicle modelling errors are considered, a Self-Correction Data Fusion algorithm is developed to integrate results of the estimator and direct integral method with error correction theory. Some simulations and experiments are also implemented, and their results verify the high accuracy and good robustness of the cooperative estimation scheme.

关键词： Tyre-road friction coefficient body's sideslip angle Strong Tracking Filter interactive multiple model integral error correction

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A Fixed-Lag Smoothing interactive multiple model Tracking and Interception System for Maneuvering Target

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第2期44卷 605-615页

作者： Zeb, Nadia Hameed, Gul Manzoor, Sajjad Ullah, Ihsan Khan, Sumair Asad, Muhammad Jhangiri, Zahid Mehmood Khan, Uzair COMSATS Univ Islamabad Dept Elect & Comp Engn Abbottabad Campus Abbottabad Pakistan Mirpur Univ Sci & Technol Fac Engn Mirpur 10250 Ajk Pakistan COMSATS Univ Islamabad Dept Comp Sci Abbottabad Campus Abbottabad Pakistan

In this work, an improved interactive multiple model-based smoothing algorithm is proposed for tracking and interception of a maneuvering target. The smoothed states are obtained at some past scan (k - N), but the target interception happens at the current scan. This helps to obtain the real-time benefits of the smoothing. Fixed-lag smoothing interactive multiple model algorithm is employed to follow the target more precisely. Real-time benefit of smoothing is used to improve the decision making in order to achieve better target interception before it reaches its desired destination. Three different simulation environments have been used to analyze the performance and validity of the proposed algorithm. The simulation results are further extended to compare each case in terms of hit error and root-mean-square error.

关键词： Fixed-lag smoothing interactive multiple model Maneuvering target Interception Root-mean-square error Hit error

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A Survey of Maneuvering Target Prediction via interactive multiple model

A Survey of Maneuvering Target Prediction via Interactive Mu...

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第34届中国控制与决策会议

作者： Wang Na Peng Kun Li Yang School of Control Science and Engineering Tiangong University Key Laboratory of Intelligent Control of Electrical Equipment

Maneuvering target prediction is a kind of methods to estimate the future state of moving target with randomness and complexity,according to the information of historical moment or current *** the target maneuvering,larger prediction error will be produced by single motion *** the interactive multiple model is *** this means,several models are used to match with their moving *** the parallel filtering is realized,and the results of filters are weighted and combined to obtain the final *** the prediction accuracy of the proposed method for the more complex motions is *** this paper,considering the common states of constant velocity,constant acceleration and variable acceleration,the principle of interactive multiple model is *** then its study and application in target prediction is ***,the development and the trends of the interactive multiple model in the target prediction is analyzed and prospected.

关键词： Maneuvering Target Prediction Target State Estimation Motion model model Prediction Error interactive multiple model

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Deep feature tracking based on interactive multiple model

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NEUROCOMPUTING 2019年 333卷 29-40页

作者： Tang, Fuhui Lu, Xiankai Zhang, Xiaoyu Hu, Shiqiang Zhang, Huanlong Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Dianji Univ Sch Elect Engn Shanghai 201306 Peoples R China Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Henan Peoples R China

Existing convolutional neural network (CNN) based trackers have limited tracking performance in some challenging scenarios such as deformation, background clutter and illumination variation, because the features extracted from a single layer or from a linear combination of multiple layers are insufficient to describe the target appearance. To overcome this problem, we propose a novel tracking algorithm based on interactive multiple model (IMM) framework for better exploring deep features from different layers (IMM_DFT). In this method, we first build measurement models from convolutional layers by applying correlation filters on hierarchical features. Then, to effectively estimate the target state for each layer, we design a hybrid system which consists of the foregoing measurement model and an online learning motion model. Finally, in order to achieve the optimal fusion of the systems for adapting diverse appearance variation of the target and background, an IMM estimator is developed to dynamically adjust the weight of each system using likelihood function and transition probabilities. Extensive experiments on OTB-2013, OTB-2015 and VOT-2016 benchmark databases demonstrate that the proposed algorithm achieves more favorable performance than several state-of-the-art methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Convolutional neural network interactive multiple model Measurement model Motion model Correlation filters Tracking performance

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interactive multiple-model vertical vibration detection of structures based on high-frequency GNSS observations

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GPS SOLUTIONS 2022年第2期26卷 48-48页

作者： Shen, Nan Chen, Liang Lu, Xiangchen Ruan, Yanlin Hu, Hao Zhang, Zhetao Wang, Lei Chen, Ruizhi Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Hubei Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China

High-frequency global navigation satellite system (GNSS) observations are of great significance for structural health monitoring. At present, most studies on high-frequency GNSS observations focus on displacement extraction, while there are few studies on vibration detection. The purpose of this research is to explore a vertical vibration detection method based on high-frequency GNSS observations. The principle of GNSS kinematic positioning is presented, and a vibration detection method based on high-frequency GNSS observations is proposed. A stationary state model and a vibration state model are designed to represent the vibration state and static state of the structure. We introduce the interactive multiple-model Kalman filter to detect vibrations through model probabilities automatically. To reduce the influence of the trend term caused by unmodeled errors, we preprocess original positioning results by a moving average. To reduce the false alarm rate caused by local disturbance, a hold-on mechanism is proposed. Only when the number of the hold-on epoch is greater than the preset hold-on parameter, it is considered that vibration has occurred. Then, the detected epoch and corresponding displacement are further analyzed and processed. Simulation and field experiments were carried out to verify the proposed method. The results show the feasibility and effectiveness of the method for vertical vibration detection. Therefore, this study should be of value to practitioners wishing to implement an early warning system based on high-frequency GNSS observations.

关键词： High-frequency GNSS observations Vibration detection interactive multiple model Kalman filter

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