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Maximum correntropy quadrature Kalman filter based interacting multiple model approach for maneuvering target tracking

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-10页

作者： Liu, Bao Wu, Ziwei Xian Univ Sci & Technol Coll Elect & Control Engn 58 Yanta Middle Rd Xian 710054 Shaanxi Peoples R China

This paper proposes a Maximum Correntropy Quadrature Kalman Filter Based interacting multiple model approach (IMM-MCQKF) for the state estimation with the non-Gaussian noise and model uncertainty problem in the maneuvering target tracking. The performance of Quadrature Kalman Filter (QKF) will significantly decrease when the noise is non-Gaussian, especially under the heavy tailed noise. The proposed IMM-MCQKF approach not only solves this problem, but also ensures a good tracking accuracy with the mode switching caused by the target maneuvering. Specifically, a Maximum Correntropy Quadrature Kalman Filter (MCQKF) combines the maximum correntropy criterion and statistical linearization regression model into the QKF algorithm to increase the estimation accuracy under the non-Gaussian noise. Moreover, we introduce a fixed-point iteration method to ensure that the MCQKF algorithm has a closed solution. Secondly, the MCQKF is embedded into the interacting multiple model framework to address the problem of mode switching caused by target maneuvering. Finally, the simulation results demonstrate the tracking effectiveness of the IMM-MCQKF in maneuvering target tracking scenarios with non-Gaussian noise compared with IMM-EKF, IMM-UKF, IMM-CKF, IMM-QKF, AIMM-UKF, and IMM-AMTP algorithms.

关键词： Non-Gaussian Quadrature Kalman filter interacting multiple model Maximum correntropy criterion Maneuvering target tracking

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Enhanced tire road friction coefficient estimation through interacting multiple model design based on tire force observation

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NONLINEAR DYNAMICS 2025年第13期113卷 15925-15941页

作者： Wan, Wenkang Sheng, Kai Cai, Qing Ao, Lei Ouyang, Nan Feng, Haoxuan Sime, Dejene M. Xidian Univ Acad Adv Interdisciplinary Res Xian 710000 Peoples R China Xidian Univ Sch Elect Engn Xian 710000 Peoples R China Geely Automobile Res Inst Ningbo 315000 Peoples R China

Accurately understanding the tire-road friction coefficient (TRFC) interaction is crucial for enhancing overall vehicle control performance. However, existing estimation methods heavily rely on accurate tire modeling, which may fail to predict TRFC effectively under conditions of poor modeling accuracy or varying operational environments. This paper introduces an interacting multiple-model estimation method for TRFC, leveraging tire force observations. Firstly, taking advantage of the observation capabilities of distributed drive electric vehicles, a longitudinal tire force estimator with unknown inputs is designed. Simultaneously, a lateral tire force observer based on adaptive sliding mode observer is constructed, making full use of the vehicle's onboard sensor data. A TRFC estimator is proposed by employing a square-root cubature Kalman filter, incorporating square-root filtering to reduce algorithm complexity while maintaining accuracy. Finally, an interacting multiple-model mechanism is specifically designed to handle both pure longitudinal dynamics and combined operating conditions. The proposed algorithm is co-simulated by Carsim-Matlab and further tested in a real vehicle equipped with a 6-axis wheel transducer. The results show that the algorithm proposed in this paper can accurately estimate the tire force and TRFC across various maneuvering and road scenarios. Compared to traditional algorithms, the proposed approach demonstrates superior estimation accuracy and robustness.

关键词： Distributed drive electric vehicle Cubature kalman filter interacting multiple model State estimation Tire road friction coefficient

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Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with interacting multiple model and Informer Framework

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SENSORS 2025年第8期25卷 2531-2531页

作者： Li, Fan Xu, Xuezhi Wang, Rihan Ma, Mingyuan Dong, Zijing Civil Aviat Flight Univ China CAAC Key Lab Flight Tech & Flight Safety Guanghan 618307 Peoples R China Civil Aviat Flight Univ China Coll Air Traff Management Guanghan 618307 Peoples R China Sichuan Univ Sch Math Chengdu 610065 Peoples R China

Aircraft trajectory prediction is challenging because of the flight process with uncertain kinematic motion and varying dynamics, which is characterized by intricate temporal dependencies of the flight surveillance data. To address these challenges, this study proposes a novel hybrid prediction framework, the IMM-Informer, which integrates an interacting multiple model (IMM) approach with the deep learning-based Informer model. The IMM processes flight tracking with multiple typical motion models to produce the initial state predictions. Within the Informer framework, the encoder captures the temporal features with the ProbSparse self-attention mechanism, and the decoder generates trajectory deviation predictions. A final fusion combines the IMM estimators with Informer correction outputs and leverages their respective strengths to achieve accurate and robust predictions. The experiments are conducted using real flight surveillance data received from automatic dependent surveillance-broadcast (ADS-B) sensors to validate the effectiveness of the proposed method. The results demonstrate that the IMM-Informer framework has notable prediction error reductions and significantly outperforms the prediction accuracies of the standalone sequence prediction network models.

关键词： flight trajectory ADS-B data hybrid prediction interacting multiple model Informer

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Vehicle trajectory extraction with interacting multiple model for low-channel roadside LiDAR

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 262卷

作者： Gong, Bowen Zhao, Binwen Wang, Yue Lin, Ciyun Liu, Hongchao Jilin Univ Dept Traff Informat & Control Engn Changchun 130022 Peoples R China Jilin Engn Res Ctr Intelligent Transportat Syst Changchun 130022 Peoples R China Texas Tech Univ Dept Civil Environm & Construct Engn Lubbock TX 79409 USA

High-precision and consistent vehicle trajectories encompass microscopic traffic parameters, mesoscopic traffic flow characteristics, and macroscopic traffic flow features, which is the cornerstone of innovation in data-driven traffic management and control applications. However, occlusion and trajectory interruption remain challenging in multivehicle tracking under complex traffic environments using low-channel roadside LiDAR. To address the challenge, a novel framework for vehicle trajectory extraction using low-channel roadside LiDAR was proposed. First, the geometric features of the cluster and its L-shape bounding box were used to address the oversegmentation in vehicle detection arising from occlusion and point cloud sparse. Then, objects within adjacent point cloud frames were associated by developing an improved Hungarian algorithm integrated with an adaptive distance threshold to solve the mismatching problem caused by objects entrancing and exiting in a new point cloud frame. Finally, an improved interacting multiple model by considering vehicle driving patterns was deployed to predict the location of missing vehicles and connect the interrupted trajectories. Experimental results showed that the proposed methods achieve 98.76 % of vehicle detection accuracy and 97.40 % of data association precision. The mean absolute error (MAE) and mean square error (MSE) of the vehicle position estimation are 0.2252 m and 0.0729 m2, respectively. The trajectory extraction precision outperforms most of the state-ofthe-art algorithms.

关键词： Trajectory extraction Vehicle detection Vehicle matching and tracking Longitudinal control model interacting multiple model

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interacting multiple model adaptive robust Kalman filter for process and measurement modeling errors simultaneously

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SIGNAL PROCESSING 2025年 227卷

作者： Yang, Baojian Wang, Huaiguang Shi, Zhiyong Army Engn Univ PLA Shijiazhuang Campus Shijiazhuang 050003 Peoples R China

This paper proposes an effective Interactive multiple model Adaptive Robust Kalman Filter (IMMARKF) without time delay to handle situations where both process modeling errors and measurement modeling errors exist simultaneously. Building upon the robust Centered Error Entropy Kalman Filter (CEEKF) for outlier measurements and the Adaptive Kalman Filter (AKF) for process modeling errors, the IMMARKF method combines the Gaussian optimality of the KF, the adaptability of AKF, and the robustness of CEEKF using the interacting multiple model (IMM) principle to adapt reasonably to changing application environments, and can obtain estimation results in the absence of time delay. Target tracking simulations show that compared to existing methods, the proposed method can better adapt to non-stationary noise and application environments where process anomalies and measurement anomalies occur simultaneously.

关键词： Kalman filter Robust filter Adaptive filter Centered error entropy interacting multiple model

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Adaptive interacting multiple model particle filter based on a dual-pattern bat algorithm for maneuvering target tracking

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2025年第4期239卷 293-311页

作者： Tian, Mengchu Chen, Zhimin Fang, Yubin Nanjing Univ Sci & Technol Sch Intelligent Mfg 200 Xiaolingwei St Nanjing 210094 Peoples R China China Satellite Maritime Tracking & Controlling De Jiangyin Peoples R China

The interacting multiple model particle filter (IMM-PF) is a filtering method commonly used for nonlinear non-Gaussian radar systems. Its transfer probabilities are usually fixed and dependent on prior information. When tracking maneuvering targets, the IMM-PF may cause excessive tracking errors due to the model switching lag. In addition, the particle impoverishment caused by the resampling of the particle filter seriously affects the filtering accuracy. Therefore, the IMM-PF has difficulty meeting the accuracy and speed requirements of modern high-performance radar target tracking systems. To address these problems, an adaptive interacting multiple model particle filter based on the dual-pattern bat algorithm (ADIMM-DPBA-PF) is proposed for tracking maneuvering targets under nonlinear and non-Gaussian conditions. First, the adaptive model switching mechanism is established to adjust the transition probabilities using the model probability posterior information at consecutive times, improving the model switching efficiency. Second, a particle filter based on the dual-pattern bat algorithm (DPBA-PF) is proposed. The filter exploits global search and local search strategies to optimize the particles and intelligently move the particles to the high likelihood region. Finally, a particle filter based on the dual-pattern bat algorithm is used to form the adaptive interacting multiple model method. The experimental results show that the proposed algorithm has better comprehensive performance than the IMM-PF.

关键词： interacting multiple model maneuvering target tracking particle filter bat algorithm filtering algorithm

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interacting multiple model Based Detector to Compensate Power Amplifier Distortions in Cognitive Radio

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IEEE TRANSACTIONS ON COMMUNICATIONS 2015年第5期63卷 1580-1593页

作者： Ben Mabrouk, Mouna Ferre, Guillaume Grivel, Eric Deltimple, Nathalie Bordeaux INP UMR 5218 IMS Lab Bordeaux France

For a battery driven terminal, the power amplifier (PA) efficiency must be optimized. Consequently, non-linearities may appear at the PA output in the transmission chain. To compensate these distortions, one solution consists of using a digital detector based on a Volterra model of both the PA and the channel and a Kalman filter (KF) based algorithm to jointly estimate the Volterra kernels and the transmitted symbols. Here, we suggest addressing this issue when dealing with cognitive radio (CR). In this case, additional constraints must be taken into account. Since the CR terminal may switch from one sub-band to another, the PA non-linearities may vary over time. Therefore, we propose to design a digital detector based on an interacting multiple model combining various KF based estimators using different model parameter dynamics. This makes it possible to track the time variations of the Volterra kernels while keeping accurate estimates when those parameters are static. Furthermore, the single and multicarrier cases are addressed and validated by simulation results. Our solution corresponds to a compromise between computational cost and bit-error-rate performance.

关键词： Power amplifier digital detector cognitive radio Volterra model Kalman filtering interacting multiple model single carrier multicarrier

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interacting multiple model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter

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IEEE ACCESS 2020年 8卷 81034-81044页

作者： Liu, Xuhang Liu, Xiaoxiong Zhang, Weiguo Yang, Yue Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

To improve the precision and robustness of Unmanned Aerial Vehicle (UAV) integrated navigation systems, this paper presents an interacting multiple model (IMM) navigation algorithm based on a Robust Cubature Kalman Filter (RCKF) with modified Zero Velocity Update (ZUPT) method assistance. This algorithm has a two-level fusion structure. At the bottom level, the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation model and the Dynamic Zero Velocity Update/Inertial Navigation System (DZUPT/INS) integrated navigation model are established by modifying the Zero Velocity Update (ZUPT) method. Subsequently, the RCKF algorithm adopts a robust factor to weaken the influence of measurement outliers on the filter solution. At the top level, the estimation results of the GPS/INS integrated navigation model and the DZUPT/INS integrated navigation model are fused by the IMM algorithm. In addition to enhancing the robustness of filter estimation in the presence of measurement outliers, the proposed navigation algorithm also corrects navigation errors with ZUPT method assistance. Simulation and experimental analyses demonstrate the performance of the proposed navigation algorithm for UAVs.

关键词： interacting multiple model Robust Cubature Kalman Filter Dynamic Zero Velocity Update integrated navigation

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interacting multiple model methods in target tracking: A survey

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1998年第1期34卷 103-123页

作者： Mazor, E Averbuch, A Bar-Shalom, Y Dayan, J Technion Israel Inst Technol Dept Mech Engn IL-32000 Haifa Israel Tel Aviv Univ Sch Math Sci IL-69978 Tel Aviv Israel Univ Connecticut Dept Elect & Syst Engn Storrs CT 06269 USA

The interacting multiple model (IMM) estimator is a suboptimal hybrid biter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is its ability to estimate the state of a dynamic system with several behavior modes which can "switch" from one to another. In particular, the IMM estimator can be a self-adjusting variable-bandwidth filter, which makes it natural for tracking maneuvering targets. The importance of this approach is that it is the best compromise available currently between complexity and performance: its computational requirements are nearly linear in the size of the problem (number of models) while its performance is almost the same as that of an algorithm with quadratic complexity. The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems. Special attention is given to the assumptions underlying each algorithm and its applicability to various situations.

关键词： computational complexity military computing military systems self-adjusting systems sensor fusion state estimation target tracking tracking computational requirements cost-effective hybrid state estimation dynamic system interacting multiple model maneuv self-adjusting systems model Laws tracking Target tracking algorithms Dynamic systems complexity classes State estimation Sensor fusion IMM military computing

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interacting multiple model Based on Maximum Correntropy Kalman Filter

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第8期68卷 3017-3021页

作者： Fan, Xuxiang Wang, Gang Han, Jiachen Wang, Yinghui Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Traditional interacting multiple model Kalman filter (IMM-KF) was derived for linear discrete time system with Markovian coefficients and it works well under Gaussian noise. In practice, when the system is affected by the impulsive noise, the performance of the traditional IMM-KF model becomes poor. Then, maximum correntropy Kalman filter (MCKF) which has advantages in dealing with non-Gaussian noise was proposed. However, the performance of traditional MCKF becomes poor when the motion state is not single. In this brief, we propose a new algorithm called interacting multiple model based on maximum correntropy Kalman filter (IMM-MCKF) by combining interacting multiple model (IMM) with MCKF to deal with the impulsive noise. Also, the traditional MCKF works poorly when the kernel bandwidth is small, we made a further improvement by changing the kernel. Simulation examples are given to demonstrate the effectiveness of the new algorithms.

关键词： Mathematical model Kalman filters Kernel Covariance matrices Filtering Target tracking Integrated circuit modeling Maximum correntropy Kalman filter maximum correntropy criterion interacting multiple model mixed-kernel root mean square error

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