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multi-Maneuvering Sources DOA Tracking With Improved interactive multi-model multi-Bernoulli Filter for Acoustic Vector Sensor (AVS) Array

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第8期70卷 7825-7838页

作者： Dong, Xudong Zhang, Xiaofei Zhao, Jun Sun, Meng Wu, Qihui Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201800 Peoples R China

To solve the problem of the direction of arrival (DOA) maneuvering in acoustic vector sensor (AVS) array, we propose an interactive multi-model multi-target multi-Bernoulli (IMM-MeMBer) two-dimensional (2-D) DOA tracking algorithm. The idea of the IMM algorithm is employed to interactively estimate the predicted sampled particles at the expense of calculating the likelihood function of the predicted particles by pseudo-spectrum of multi-signal classification (MUSIC). The proposed method fully considers various possibilities of target motion, which makes the target state estimation more effective. Moreover, the de-noising and exponential weighting of MUSIC pseudo-spectrum improve the likelihood function of particles, thus enhancing the weight of particles in high likelihood region and making these particles more effective in the subsequent resampling process. Simulation results show that the IMM-MeMBer algorithm has better performance than projection approximation subspace tracking of deflation MUSIC (PASTD-MUSIC), MeMBer, particle filter (PF), and random finite set PF (RFS-PF) in the DOA tracking of maneuvering sources.

关键词： Radar tracking Target tracking Direction-of-arrival estimation Signal processing algorithms multiple signal classification Acoustics Sensor arrays Acoustic vector sensor interactive multi-model DOA tracking multi-target multi-bernoulli

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interactive multi-model Target Maneuver Tracking Method Based on the Adaptive Probability Correction 9th

Interactive Multi-model Target Maneuver Tracking Method Base...

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9th International Conference on Swarm Intelligence (ICSI)

作者： Ren, Jiadong Zhang, Xiaotong Sun, Jiandang Zeng, Qingshuang Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China Shanghai Inst Spaceflight Control Technol Shanghai 201109 Peoples R China Shanghai Key Lab Space Intelligent Control Techno Shanghai 201109 Peoples R China

ISBN: (纸本)9783319938189;9783319938172

Non-cooperative target tracking is a key technology for complex space missions such as on-orbit service. To improve the tracking performance during the unknown maneuvering phase of the target, two methods including the IMM (interactive multi-model) algorithm based on extended CW equation and the variable IMM algorithm based on CW and extended CW equation are presented. The analysis and simulation results show that the higher the maneuvering index of the target is, the more obvious the advantages of the classical augmented IMM method are. However, the variable dimension IMM method has consistent performance for all the maneuver index interval of the target, and it is relatively suitable for engineering applications due to the lower complexity of algorithm.

关键词： Augmentation Relative navigation Target maneuver interactive multi-model

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Iterative Unbiased Conversion Measurement Kalman Filter with interactive multi-model Algorithm for Target Tracking 18th

Iterative Unbiased Conversion Measurement Kalman Filter with...

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18th Annual Asia Simulation Conference (AsiaSim)

作者： Li, Da Zou, Xiangyu Lou, Ping Li, Ruifang Wei, Qin Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Key Lab Fiber Opt Sensing Technol & Informat Proc Minist Educ Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9789811328534;9789811328527

For modern tracking systems, the tracking target generally has the characteristics of high speed and mobility. Tracking targets has always been a challenging problem, especially tracking high speed and strong maneuvering targets, which is difficult in theory and practice. An interactive multi-model (IMM) based on iterative unbiased conversion measurement Kalman filter (IUCMKF) is proposed. The new algorithm takes advantages of the interactive and complementary characteristics between different models to overcome the problems of low precision and filter divergence. First, investigate the function of conversion measurement Kalman filter (CMKF), debiased conversion measurement Kalman filter (DCMKF), and IUCMKF on double-model and multiple-model. Secondly, compare and analyze the performance of the three algorithms (CMKF-IMM, DCMKF-IMM and IUCMKF-IMM). Finally, identify the effect and impact of the combination of the four different models including CA, Singer, CS, and Jerk on the accuracy of target tracking. The results of numerical simulation show the choice of the number and type of models should be weighed according to the actual simulation environment. Even though more choices of models can improve the tracking accuracy of the target, but that also greatly increases the complexity of the algorithm and the error consistency of the algorithm also cannot be guaranteed to some extent. Therefore, compared with CMKF-IMM and DCMKF-IMM, the new algorithm can attain a more accurate state of the target being tracked and the covariance estimation. It also has more potential in improving tracking accuracy.

关键词： Target tracking interactive multi-model Iterative unbiased conversion measurement Kalman filter

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Recovery of missing strain data under multiple time-varying effects using IMM Kalman filtering

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ENGINEERING STRUCTURES 2025年 329卷

作者： Gao, Ke Weng, Shun Zhu, Hong-Ping Chen, Chao-Jun Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Peoples R China Hubei Key Lab Control Struct Wuhan Peoples R China Natl Ctr Technol Innovat Digital Construct Wuhan Peoples R China China Railway Steel Struct Co Ltd Nanjing Peoples R China

Data loss usually occurs in field structural health monitoring (SHM), which leads to difficulties in the data analysis and safety assessment of structures during the construction stage. Since the state model of structural deformation is complicated with multiple time-varying effects coupled, existing data recovery method based on a single state model is not capable to accurately recover long-term strain loss due to the perturbations and uncertainties in the state model. This study proposes an effective data recovery method for recovering missing strain based on the interactive multi-model (IMM) Kalman filtering. The estimation error brought by the model uncertainty is reduced by the fusion of multiple models. The accuracy and effectiveness of the proposed method were verified through a concrete shrinkage and creep experiment. The results showed that the recovered data is in good agreement with the real data within long-term missing zone. Furthermore, this method was applied to a real super high-rise building to recover the missing strain data during the construction stage. The recovered strain data provide valuable assistance to the construction managers to understand the complete evolution of the structural strain under time-varying effects during the construction process.

关键词： Structural health monitoring Data recovery Strain measurement Kalman filtering interactive multi-model

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A fast implementation of interactive-model generalized labeled multi-bernoulli filter for interval measurements

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SIGNAL PROCESSING 2019年 164卷 345-353页

作者： Wu, Sun-yong Dong, Xu-dong Zhao, Jun Sun, Xi-yan Cai, Ru-hua Guilin Univ Elect Technol Sch Math & Comp Sci Guilin 541004 Peoples R China Guangxi Informat Sci Expt Ctr Guilin 541004 Peoples R China

Aiming at tracking multi-maneuvering targets for interval measurements, we introduce the multi-model concept into the Generalized Labeled multi-Bernoulli (GLMB) algorithm and propose an interactive multi-model GLMB (IMM-GLMB) tracking algorithm for interval measurements. The sampled particles are predicted by the interactive multi-model (IMM) algorithm. Afterwards, we update the predicted particles by introducing a generalized likelihood function in conjunction with the GLMB filter update strategy. With the combination of the characteristics of GLMB filtering and the IMM method, this algorithm can effectively improve the target state prediction accuracy and avoid tracking failure caused by model mismatch in the maneuvering process of targets, but it requires different truncation step for each component in the prediction and update. In order to improve the efficiency of the algorithm, we adopt the idea of a fast algorithm proposed in the literature and propose the IMM Generalized Labeled multi-Bernoulli Fast (IMM-GLMBF) algorithm. This IMM-GLMBF algorithm integrates the prediction and the update steps of the IMM-GLMB algorithm, so that it requires only one truncation. Compared to the IMM-GLMB algorithm, the proposed IMM-GLMBF algorithm is more accurate in estimating the number of targets and their states, and it greatly reduces the computation cost. In addition, we present the Sequential Monte Carlo (SMC) implementation of the IMM-GLMB and IMM-GLMBF algorithms. (C) 2019 Published by Elsevier B.V.

关键词： Random finite set multi-maneuvering target tracking Interval measurements interactive multi-model Generalized labeled multi-bernoulli filter

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A multimodal fusion positioning method with adaptive driving behavior for intelligent vehicles

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年第14期238卷 4626-4645页

作者： Zhang, Ziyu Wang, Chunyan Zhao, Wanzhong Cao, Mingchun Liu, Jinqiang Nanjing Univ Aeronaut & Astronaut Dept Vehicle Engn Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Dept Vehicle Engn 29 YuDao St Nanjing 210016 Peoples R China

Due to the difficulty of Global Navigation Satellite System (GNSS) in urban canyon environments, GNSS and inertial navigation system (INS) fusion positioning is widely used in the field of intelligent driving. In actual positioning, INS usually uses the vehicle physical model as a constraint to calculate inertial measurement unit (IMU) data to supplement the vehicle position and attitude as GNSS positioning information. However, the existing algorithms usually use a fixed physical model. When the vehicle's driving behavior changes, such as lane-changing, acceleration, deceleration, etc., the fixed model cannot provide long-term constraints on cumulative errors, resulting in a decrease in fusion positioning accuracy and stability. Therefore, this paper proposes a multimodal fusion positioning method with adaptive driving behavior for intelligent vehicles. First, to adapt to multimodal driving behavior, a multimodal assisted positioning physical model with adaptive driving behavior (MAPM) is established, which expresses different physical models probabilistically based on driving behavior and historical data. Secondly, high-precision estimation of vehicle positioning parameters is achieved based on model probability and interactive multi-model (IMM). Finally, effective fusion of MAPM/GNSS/INS positioning information is achieved under the federated Kalman filtering framework. The simulation results show that compared to traditional GNSS/INS fusion positioning, the proposed method can effectively improve positioning accuracy and stability, especially in driving conditions with changes in driving behavior.

关键词： GNSS/INS fusion positioning multimodal driving behavior interactive multi-model assisted positioning physical model

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Lane change trajectory prediction considering driving style uncertainty for autonomous vehicles

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 206卷

作者： Chen, Guoying Gao, Zheng Hua, Min Shuai, Bin Gao, Zhenhai Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

Lane change trajectory prediction is crucial for autonomous vehicles (AVs) to assess their own driving safety in advance. However, there are significant uncertainties in the implementation of such prediction, including different behaviors caused by agent-agent interaction and driving styles of drivers. While prototype trajectories can serve as a means to represent typical motion patterns and enhance trajectory prediction performance, their utilization in modeling motion patterns tends to overlook the influence of agent-agent interactions and vehicle dynamics. This paper proposes a fusion algorithm that considers driving style and vehicle dynamics to address these uncertainties. The algorithm involves a long short-term memory (LSTM) lane change behavior recognition model that mines key features of agent-agent interaction through the attention mechanism. The Gaussian process (GP) motion modeling trajectory prediction (GPMM-TP) algorithm considers the driving style of the prototype trajectory based on the behavior recognition results. To further improve short-term and long-term prediction, the interactive multi-model (IMM) algorithm is used to assign probability weights to the GP model and the Extended Kalman Filter (EKF) model based on prediction accuracy, taking into account the driving styles and the vehicle dynamics. The proposed algorithm provides a promising approach to improving the accuracy of lane change trajectory prediction for AVs, and its effectiveness is demonstrated using the HighD dataset.

关键词： Attention mechanisms Lane change behavioral recognition Driving style Gaussian processes interactive multi-model Trajectory prediction

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A multi-Interaction Filtering Control Game model for Close-in Shipborne Gun Weapon System 24

A Multi-Interaction Filtering Control Game Model for Close-i...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Wu, Hanwen Hou, Yangyang Yang, Luhui Unit 92941 Peoples Liberat Army Huludao Peoples R China Jiangsu Automat Res Inst Lianyungang Peoples R China

ISBN: (纸本)9798400718144

In the realm of future warfare, the inevitable trend towards missile informationization underscores the significance of technological advancements. Within this context, the comprehensive automation of close-in shipborne gun weapon systems holds immense potential to enhance the interception capabilities against anti-ship missiles. This paper delves into the development trajectory of cutting-edge missile technology systems, emphasizing the integration of multi-source data fusion sensing, mobile surprise control, and collaborative game decision-making. Consequently, it identifies the urgent need for the development of autonomous control and interception capabilities within close-in shipborne gun weapon systems. Furthermore, a multi-Interaction filtering control game model for close-in shipborne gun weapon systems is proposed. This model encompasses adaptive interactive multi-model filtering, enabling it to adapt to a diverse range of target maneuvers. It also incorporates smoother and more precise naval gun follower control, multi-target interception game decision-making, and autonomous collaborative decision-making among formation weapons. This comprehensive approach not only offers a research framework for the development of close-in shipborne gun weapon systems but also serves as a foundation for the simulation and testing platforms of the future.

关键词： multi-source Data Fusion interactive multi-model Game Theory Filtering Algorithm

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Resilient interactive Sensor-Independent-Update Fusion Navigation Method

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 16433-16447页

作者： Meng, Qian Hsu, Li-Ta Southeast Univ Minist Educ Sch Instrument Sci & Engn Key Lab Microinertial Instrument & Adv Nav Techno Nanjing 210096 Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China

To improve the robustness and reliability of multi-sensor navigation and reduce the uncertainties and complexity of sensor management in challenging environment, a resilient interactive sensor-independent-update (ISIU) method is proposed. Inspired by the interactive cooperation theory, the contributions can be divided into two aspects. Firstly the priority of trust of navigation sensors is introduced into the information fusion in the form of transition probability matrix defined by Markov chain. Secondly every observable sensor is integrated with the propagated system in an elemental filter with sensor-independent-update structure. The multi-sensor integration is implemented in state estimation domain enhanced by interactive information fusion rather than in measurement domain implemented in traditional filter method. The overall estimation is determined by the weighted sum of average from every filter estimate. This weight of every model is dynamic updated by the prior transition information and posterior model likelihood. The same independent structure is also applied to adopt new available sensor to realize plug-and-play navigation. The kinematic vehicle experiment in sub-urban and urban canyon environment verified the superiority of the proposed method. The ISIU method shows better accuracy and reliability compared to classical Kalman filters. The introduction of priority of sensors and decoupled measurement update process make it robust and insensitive to sensor measurement noise and outliers. The interactive sensor-independent-update structure has the natural function of fault detection and exclusion without additional operations. The effect of dynamic sensor selection is achieved in this processing. The proposed ISIU method is pretty suitable for resilient navigation in challenging environments.

关键词： Navigation Reliability Vehicle dynamics Monitoring Robustness Heuristic algorithms Estimation interactive multi-model Kalman filter multi-sensor integration plug and play resilient navigation sensor selection

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Detection of non-cooperative target manoeuvres in space based on collaborative multi-star observations 74

Detection of non-cooperative target manoeuvres in space base...

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74th International Astronautical Congress, IAC 2023

作者： Gao, Shaotian Ma, Weihua Luo, Jianjun Yuan, Jing National Key Laboratory of Aerospace Flight Dynamics Northwestern Polytechnical University 127 Youyixi Street Shaanxi Xi'an710072 China

Space targets with communication failure present non-cooperative characteristics, and in order to ensure the safety of on-orbit spacecraft, it is necessary to sense the motion state of non-cooperative targets in long-distance space. In this regard, this paper proposes a motion detection method for space targets based on IMM-EKF. multiple observation satellites are used to observe the target at the same time, and the target altitude and azimuth information measured from multiple directions are used as the observation information;the IMM algorithm combined with different manoeuvre models is used to estimate and track the target's motion pattern, and the EKF algorithm is used to filter and predict the target's position and velocity. The simulation results show that the method can effectively detect and track the manoeuvre behaviour of space targets with high accuracy and robustness, which is of great significance for the safe maintenance of space targets. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： coordinated observation extended kalman filter interactive multi-model state estimation

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