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interacting multiple model-based ETUKF for efficient state estimation of connected vehicles with V2V communication

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Green Energy and Intelligent Transportation 2023年第1期2卷 39-47页

作者： Yan Wang Zhongxu Hu Shanhe Lou Chen Lv School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore

Accurate prediction of the motion state of the connected vehicles,especially the preceding vehicle(PV),would effectively improve the decision-making and path planning of intelligent *** evolution of vehicle-tovehicle(V2V)communication technology makes it possible to exchange data between ***,since V2V communication has a transmission interval,which will result in the host vehicle not receiving information from the PV within the time ***,V2V communication is a time-triggered system that may occupy more communication bandwidth than *** the other hand,traditional estimation methods of the PV state based on individual models are usually not applicable to a wide range of driving *** address these issues,an event-triggered unscented Kalman filter(ETUKF)is first employed to estimate the PV state to strike a balance between estimation accuracy and communication ***,an interactive multi-model(IMM)approach is combined with ETUKF to form IMMETUKF to further improve the estimation accuracy and appli***,simulation experiments under different driving conditions are implemented to verify the effectiveness of *** test results indicated that the IMMETUKF has high estimation accuracy even when the communication rate is reduced to 14.84%and the proposed algorithm is highly adaptable to different driving conditions.

关键词： Preceding vehicle state estimation interacting multiple model Event triggered unscented Kalman V2V communication

Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第8期8卷 1277-1282页

作者： DU Shi-chuan SHI Zhi-guo ZANG Wei CHEN Kang-sheng Department of Information and Electronie Engineering Zhejiang University Hangzhou 310027 China

In airborne tracking,the blind Doppler makes the target undetectable,resulting in tracking difficulties. In this paper,we studied most possible blind-Doppler cases and summed them up into two types:targets' intentional tangential flying to radar and unintentional flying with large tangential speed. We proposed an interacting multiple model(IMM) particle filter which combines a constant velocity model and an acceleration model to handle maneuvering motions. We compared the IMM particle filter with a previous particle filter solution. Simulation results showed that the IMM particle filter outperforms the method in previous works in terms of tracking accuracy and continuity.

关键词： interacting multiple model Particle filter Blind Doppler

GPS/BDS/INS tightly coupled integration accuracy improvement using an improved adaptive interacting multiple model with classified measurement update

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Chinese Journal of Aeronautics 2018年第3期31卷 556-566页

作者： Houzeng HAN Jian WANG Mingyi DU Key Laboratory for Urban Geomatics of National Administration of Surveying Mapping and Geoinformation School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture

An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the *** this research, an Adaptive interacting multiple model（AIMM） filter is developed to enhance performance. The soft-switching property of interacting multiple model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential *** results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.

关键词： Adaptive filtering BeiDou navigation satellite system (BDS) Classified measurement update Global positioning system (GPS) Inertial navigation system (INS) interacting multiple model Tightly coupled

Shipborne radar maneuvering target tracking based on the variable structure adaptive grid interacting multiple model

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第9期14卷 733-742页

作者： Zheng-wei ZHU School of Information Engineering Southwest University of Science and Technology Mianyang 621010 China

The trajectory of a shipbome radar target has a certain complexity, randomness, and diversity. Tracking a strong maneuvering target timely, accurately, and effectively is a key technology for a shipbome radar tracking system. Combining a variable structure interacting multiple model with an adaptive grid algorithm, we present a variable structure adaptive grid inter- acting multiple model maneuvering target tracking method. Tracking experiments are performed using the proposed method for five maneuvering targets, including a uniform motion - uniform acceleration motion target, a uniform acceleration motion - uni- form motion target, a serpentine locomotion target, and two variable acceleration motion targets. Experimental results show that the target position, velocity, and acceleration tracking errors for the five typical target trajectories are small. The method has high tracking precision, good stability, and flexible adaptability.

关键词： Shipbome radar Target tracking Variable structure interacting multiple model Adaptive grid algorithm

Method for Underwater Target Tracking Based on an interacting multiple model

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Geo-Spatial Information Science 2008年第3期11卷 186-190页

作者： XU Weiming LIU Yanchun YIN Xiaodong Department of Electronic Engineering Tsinghua University Qinghuayuan Beijing 10084 China Department of Hydrography ＆ Cartography Engineering Dalian Naval Academy 667 Jiefang Road Dalian 116018 China.

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.

关键词： underwater target tracking interacting multiple model fuzzy logic inference

interacting multiple model Unscented Kalman Filter

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Interacting Multiple Model Unscented Kalman Filter

interacting multiple model Algorithm Using Skew-t Measurement Noise and Variational Bayesian Approach

第二十四届中国控制会议

作者： Yang Feng, Pan Quan, Liang Yan, Ye Liang (1. College of Automation, Northwestern Polytechnical University, XT an 710072, China)

＜正＞ interacting multiple model （IMM） estimator is one of the most effective methods for maneuvering target tracking. In practical applications, target tracking often does as a nonlinear estimation problem. Linearization of filter model should be done if IMM is used in nonlinear estimation. interacting multiple model unscented Kalman filter （IMMUKF） is presented for nonlinear maneuvering target tracking. IMMUKF possesses the merits of IMM for maneuvering target tracking and UKF for nonlinear estimation. Simulation is presented to evaluate the performance of the algorithm. Compared with IMMEKF, the new method gets better performance.

关键词： interacting multiple model unscented Kalman filter maneuvering target tracking nonlinear estimation

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Interacting Multiple Model Algorithm Using Skew-t Measuremen...

An Improved Robust interacting multiple model Algorithm for Underwater Acoustic Navigation

第37届中国控制会议

作者： Wang Wei Naval university of engineering school of science

Based on linear discrete-time state-space models with Skew-t measurement noise and Gaussian process noise, this paper proposes an interacting multi-model（IMM） filtering algorithm. This algorithm uses a variational Bayesian approach to approximate the posterior distribution of models. In simulations, the proposed IMM algorithm is compared with several single model filtering algorithms using variational Bayesian approach, which shows the superiority and effectiveness of this IMM algorithm.

关键词： Target Tracking interacting multiple model Measurement Noise Variational Bayesian Approach

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An Improved Robust Interacting Multiple Model Algorithm for ...

IMM Robust Cardinality Balance Multi-Bernoulli Filter for multiple Maneuvering Target Tracking with Interval Measurement

第十三届中国卫星导航年会

作者： Jianxu Shu Tianhe Xu Junting Wang Yangfan Liu Mowen Li School of Geological Engineering and Geomatics Chang'an University School of space science and physics Shandong University

In underwater acoustic long baseline navigation, the motion state of underwater vehicle is changeable and there are abnormal observations, which reduces the navigation accuracy of traditional filtering algorithm and cannot meet the demand of high precision underwater navigation. To solve this problem, this paper proposes an improved robust interacting multiple model algorithm. Firstly, the robust estimation of each model is carried out, and the equivalent variance and model probability of each model are obtained. The weight of equivalent variance is determined according to the model probability, and the mixed equivalent variance is obtained by weighted summation of equivalent variance. The mixed equivalent variance is used to replace the measurement noise variance in each model for state estimation, and the interacting robust estimation is realized. Then the model probability is updated, and the power function with faster growth rate is used to replace the matching model probability to realize the model probability correction. Finally, the final state estimation value and its covariance matrix are obtained by weighted summation of the results of the interacting robust estimation of each model according to the revised model probability, so as to improve the navigation accuracy and stability of underwater vehicles. By processing simulation and measured data, the results show that compared with Kalman filter, interactive multi-model and robust interactive multi-model, in the simulation data, the navigation error of the proposed method is reduced by 64.18 %, 26.35 % and 10.61 %, respectively;in the measured data, the navigation accuracy of this method is improved by 31.79 %, 14.76 % and 14.93 % respectively, and the navigation accuracy reaches 3.7687 m in 3 km × 3 km. Compared with the traditional filtering algorithm, the improved robust interacting multiple model algorithm can significantly improve the navigation accuracy and stability of underwater vehicles.

关键词： underwater acoustic navigation long baseline navigation system interacting multiple model robust kalman filtering model probability correction

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Chinese Journal of Electronics 2021年第6期30卷 1141-1151页

作者： YANG Biao ZHU Shengqi HE Xiongpeng National Laboratory of Radar Signal Processing Xidian University

This paper presents a novel interacting multi-model(IMM) Robust Cardinality balance multitarget multi-Bernoulli(R-CBMe MBer) filter to solve the maneuvering target tracking problem in the case of interval measurement, unknown detection probability and unknown clutter density. In essence, IMM R-CBMe MBer filter is an extended application of R-CBMe MBer *** the IMM R-CBMe MBer filter, the target state is first extended to distinguish clutter from the real *** detection probability and model probability of the target can be adaptively updated. Then, generalized likelihood function and IMM algorithm are introduced to interactively predict and update the state of the target in the IMM R-CBMe MBer filtering process. In addition,a particle application of the IMM R-CBMe MBer filter is given, and a numerical experiment is designed under nonlinear conditions. Meanwhile, Doppler information of the target is employed to estimate the velocity of each maneuvering target. Numerical experiments also verify that the IMM R-CBMe MBer filter can effectively estimate the target position, target velocity, target detection probability and clutter number in the condition of unknown detection probability, unknown clutter rate and interval measurement.

关键词： Optical, image and video signal processing Filtering methods in signal processing Computer vision and image processing techniques Other topics in statistics Other topics in statistics multiple maneuvering target tracking Random finite set interacting multiple model Cardinality balance multiple targets multi-Bernoulli filter Generalized likelihood function