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43rd Chinese Control Conference (CCC)

作者： Liu, Yanghua Fu, Rui Peng, Xiuhui Tian, Yongxiao Shanghai Univ Sch Future Technol Shanghai 200444 Peoples R China Shanghai Univ Minist Educ Key Lab Marine Intelligent Unmanned Swarm Technol Shanghai 200444 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

This paper addressed the classification and prediction problem of ship AIS trajectories through an improved interacting multiple model approach. Traditional homogeneous Markov models are characterized by static transition probabilities and have limited adaptability to trends in AIS data over time. Consequently, this study employs non-homogeneous Markov models that adapt their transition probabilities in response to dynamic changes in data characteristics, thus significantly enhancing the system's ability to adapt to temporal correlations within the data. This approach can improve the accuracy of trajectory classification and prediction. Finally, the theoretical result is applied to actual AIS trajectory processing illustrates the method's effectiveness and practical implications.

关键词： AIS Extended Kalman Filter Non-homogeneous Markov chain interacting multiple model

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On-Road Object Collision Point Estimation by Radar Sensor Data Fusion

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 14753-14763页

作者： Choi, Woo Young Lee, Seung-Hi Chung, Chung Choo Hanyang Univ Dept Elect Engn Seoul 04763 South Korea Keimyung Univ Dept Automot Syst Engn Daegu 42601 South Korea Hongik Univ Dept Mech & Syst Design Engn Seoul 04066 South Korea Hanyang Univ Div Elect & Biomed Engn Seoul 04763 South Korea

This paper proposes an object collision point estimation scheme by developing a new data fusion method in a multi-radar network environment. In order to reduce radars estimation error due to measurement uncertainty, we first design radar accuracy models determined by the position of each object. Then, an interacting multiple model (IMM) filter based on occupancy zones is designed for accurate object estimation. For a multi-radar networks object estimation, we also design a radar data fusion method using the estimated object information through the IMM instead of the object estimation information given by the radars. A collision point identification problem, where multiple sensors calculate the different vehicle surface points of the same object, is solved by developing the data fusion method to estimate the object surfaces collision point closest to the ego vehicle center. The utility of the proposed scheme was validated through a scenario-based object estimation experiment. We confirmed that the proposed data fusion method produced substantially improved error distributions over conventional methods.

关键词： Radar Data integration Estimation Radar detection Radar measurements Data models Sensor fusion Object estimation interacting multiple model data fusion radar autonomous driving

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Research on life cycle SOC estimation method of lithium-ion battery oriented to decoupling temperature

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ENERGY REPORTS 2022年 8卷 4182-4195页

作者： Wu, Yang Zhao, Hui Wang, Yongchao Li, Ran Zhou, Yongqin Harbin Univ Sci & Technol Coll Sci Harbin 150080 Heilongjiang Peoples R China Harbin Univ Sci & Technol Sch Elect & Elect Engn Harbin 150080 Heilongjiang Peoples R China Harbin Univ Sci & Technol Engn Res Ctr Automot Elect Drive Control & Syst In Minist Educ Harbin 150080 Heilongjiang Peoples R China

The efficiency, safety, and durability of lithium-ion batteries serve as the focus of energy storage systems. Among them, the accurate estimation of State of Charge (SOC) in the whole life cycle and wide temperature range is currently a capacity bottleneck that must be surpassed. In light of this issue, a joint estimation method of SOC and State of Health (SOH) based on interacting multiple model (IMM) was proposed in this paper. Through the parallel filtering process of IMM, several battery models with different temperatures and aging degrees were established. The likelihood function was then used to calculate the model probability of a single model, and the state estimates of multiple single models were fused and outputted. Then, the accurate estimation of the SOC of the whole life cycle of the lithium-ion battery oriented to the decoupling temperature was realized. Finally, the proposed method was experimentally verified on two sets of battery data with different aging degrees and temperatures. The results show that the maximum errors of SOC estimation and capacity estimation are less than 2.27% and 3.43% respectively, which meets the needs of the industry. It is proved that the proposed method can achieve accurate tracking of battery SOC and real-time estimation of battery capacity. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Battery Energy Storage System Battery Management System Lithium-ion battery State of charge State of health interacting multiple model

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Adaptive Modified Transition Probability IMM Algorithm for Maneuvering Target Tracking 9

Adaptive Modified Transition Probability IMM Algorithm for M...

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9th International Conference on Electronic Technology and Information Science (ICETIS)

作者： Hang, Gai Wang, Jianwei Nanjing Res Inst Elect Technol Nanjing Peoples R China

ISBN: (纸本)9798350388350;9798350388343

The conventional interacting multiple model algorithm (IMM) adopts a static Markov Transition Probability Matrix (TPM), which lacks sensitivity to model switching during maneuvering phase, thus causing a degradation in tracking accuracy. To address this problem, this paper introduces a novel two-stage transition probability correction method to dynamically modify the elements of the TPM. In the first stage, the correction factor is constructed using the difference in model probabilities between adjacent time steps, and each element of the TPM is individually adjusted. Furthermore, an analysis of the correction factor values under various situations is conducted. In the second stage, to avoid potential switching delays caused by the correction in the first stage, a discriminant time window is set up to determine whether the target has maneuvered, thereby directly increasing the TPM corresponding to the new matching model to expedite the switching process. Simulation results verify that the proposed method can more accurately estimate the probability of matching models during different motion periods, resulting in higher target tracking accuracy compared to several existing adaptive algorithms.

关键词： interacting multiple model maneuvering target tracking correction method transition probability

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multiple model AUV navigation methodology with adaptivity and robustness

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OCEAN ENGINEERING 2022年 254卷

作者： Zhang, Xin He, Bo Gao, Shuang Mu, Pengcheng Xu, Junchao Zhai, Ning Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao Peoples R China

Autonomous Underwater Vehicle (AUV) navigation and localization in the complex and changeable marine environment is crucial and challenging. The inaccurate noise covariance matrix may result in significant prediction errors or even filtering divergence for traditional navigation algorithms. Meanwhile, the outliers in sensor observations also have a substantial adverse effect on the AUV positioning accuracy. Therefore, in this paper, we propose an Improved interacting multiple model-Unscented Kalman Filter (IIMM-UKF) with both adaptivity and robustness for AUV navigation. Firstly, the Variational Bayesian (VB) based UKF algorithm is proposed as adaptive sub-models of IIMM to estimate the time-varying measurement noise covariance adaptively. Secondly, an outliers detector sub-model is proposed to enhance the robustness of IIMM. Gaussian Process Regression (GPR) is used to regress state pseudo values for each sub-model as estimations of the filtering process when the outliers are detected by the residual x 2 detector. multiple models work in parallel to achieve high-precision and robust AUV navigation. The performance of the IIMM-UKF algorithm has been evaluated on AUV with simulation and actual experimental data. In sea trials, the average AUV navigation accuracy of the IIMM-UKF is improved by 61.02% compared to EKF, 43.44% compared to UKF, and 35.54% compared to the IMM-UKF.

关键词： Autonomous Underwater Vehicle Navigation and localization interacting multiple model Adaptivity Robustness

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Hybrid model navigation method for autonomous underwater vehicle

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OCEAN ENGINEERING 2022年 261卷

作者： Zhang, Xin He, Bo Mu, Pengcheng Zhang, Di Ocean Univ China Fac Informat Sci & Engn 238 Songling Rd Qingdao Peoples R China

Accurate navigation and localization are essential for Autonomous Underwater Vehicles (AUVs). However, the unknown modeling errors and nonlinear errors will affect the AUV positioning accuracy. Meanwhile, the marine environment changes may not be accurately sensed by AUV. Therefore, this paper proposes a Hybrid model (HM) navigation methodology for AUV to reduce the impact of unknown errors and better predict the future states simultaneously. Firstly, an error correction sub-model based on Sequence to Sequence (Seq2Seq) predicts AUV pseudo displacements. The pseudo displacements are augmented to the observation vector to correct the unknown errors in the state estimation. Secondly, a state regression sub-model based on Gaussian Process Regression (GPR) is utilized to capture the motion trends from the historical data and regress the state variation, which is used to resist the unknown disturbances in the changeable marine environment. The sub-models work parallel with the master model, benefiting from the interacting multiple model (IMM). We compare the performance of the proposed HM to Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and IMM-EKF by using the actual experimental data of Sailfish 210 AUV. The experimental results show that the proposed HM algorithm achieves superior navigation accuracy and good fault tolerance capability.

关键词： Autonomous underwater vehicle Navigation and localization interacting multiple model Extended Kalman filter Hybrid model navigation

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Adaptive Target Tracking With interacting Heterogeneous Motion models

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 21301-21313页

作者： Na, Ki-In Choi, Sunglok Kim, Jong-Hwan ETRI Intelligent Robot Res Div Daejeon 34129 South Korea Korea Adv Inst Sci & Technol Robot Program Daejeon 34141 South Korea Seoul Natl Univ Sci & Technol SEOULTECH Dept Comp Sci & Engn Seoul 01811 South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea

multiple motion estimators such as an interacting multiple model (IMM) have been utilized to track target objects such as cars and pedestrians with diverse motion patterns. However, the standard IMM has limitations in combining motion models with different state definitions, so it cannot contain a complementary set of models that accurately work for all motion patterns. In this paper, we propose IMM-based adaptive target tracking with heterogeneous velocity representations and linear/curvilinear motion models. It can integrate four motion models with different state definitions and dimensions to be completely complimentary for all types of motions. We experimentally demonstrate the effectiveness of the proposed method with accuracy for various motion patterns using two types of datasets: synthetic datasets and real datasets. Experimental results show that the proposed method achieves the adaptive target tracking for diverse types of motion and also for various objects such as cars, pedestrians, and drones in the real world.

关键词： Adaptation models Tracking Target tracking Computational modeling Complexity theory Roads Analytical models Target tracking interacting multiple model heterogeneous motion models Bayesian filtering

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Variational Bayesian Based IMM Robust GPS Navigation Filter

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Computers, Materials & Continua 2022年第7期72卷 755-773页

作者： Dah-Jing Jwo Wei-Yeh Chang Department of Communications Navigation and Control EngineeringTaiwan Ocean University Keelung202301Taiwan TDK Taiwan Corp. YangmeiTaoyuan326021Taiwan

This paper investigates the navigational performance of Global Positioning System(GPS)using the variational Bayesian(VB)based robust filter with interacting multiple model(IMM)adaptation as the navigation *** performance of the state estimation for GPS navigation processing using the family ofKalman filter(KF)may be degraded due to the fact that in practical situations the statistics of measurement noise might *** the proposed algorithm,the adaptivity is achieved by estimating the timevarying noise covariance matrices based onVB learning using the probabilistic approach,where in each update step,both the system state and time-varying measurement noise were recognized as random variables to be *** estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB *** of the two major classical adaptive Kalman filter(AKF)approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate(MMAE).The IMM algorithm uses two or more filters to process in parallel,where each filter corresponds to a different dynamic or measurement *** robust Huber’s M-estimation-based extended Kalman filter(HEKF)algorithm integrates both merits of the Huber M-estimation methodology and *** robustness is enhanced by modifying the filter update based on Huber’s M-estimation method in the filtering *** proposed algorithm,referred to as the interactive multi-model based variational Bayesian HEKF(IMM-VBHEKF),provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors,such as the multipath *** examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.

关键词： GPS variational bayesian Huber’sM-estimation interacting multiple model adaptive outlier multipath

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A joint model and data driven track segment association algorithm for manoeuvring target tracking

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IET RADAR SONAR AND NAVIGATION 2022年第10期16卷 1670-1680页

作者： Guo, Yunfei Zhu, Junjie Zhou, Shuo Yin, Tianxiang Hangzhou Dianzi Univ Sch Automat 1158 Second St Hangzhou 310018 Peoples R China

In the field of manoeuvring target tracking, track breakage is a common issue due to strong manoeuvrability, missed detection, large measurement error, and long sampling interval, etc. In order to improve the track continuity of manoeuvring targets, a joint model and data-driven track segment association (JMDD-TSA) algorithm is proposed. First, the interacting multiple model (IMM) smoother is invoked to pre-process the track segments to obtain a smoothed state sequence. And the mode switching time (MST) is estimated by the IMM when a motion mode changing occurs in a track segment. Second, the estimated MST is used to select the partial smoothed states as the training data and the local Gaussian process (LGP) is proposed to predict and backtrack the track segments. Finally, a two-step association strategy is designed to assign and mend the candidate track segment pairs. Compared with the model-driven TSA and the data-driven TSA, the proposed algorithm can effectively improve the average track life and tracking accuracy of strong manoeuvring targets.

关键词： Gaussian process interacting multiple model joint model and data driven manoeuvring target tracking track segment association

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Probabilistic Fatigue Damage Estimation for Rotorcraft Life-Limited Components

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JOURNAL OF AIRCRAFT 2022年第2期59卷 364-376页

作者： Musso, Dakota Saetti, Umberto Rogers, Jonathan Georgia Inst Technol Woodruff Sch Mech Engn Atlanta GA 30332 USA Georgia Inst Technol Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

Condition-based maintenance programs for modern helicopters rely on algorithmic techniques to estimate the useful life remaining for life-limited components. Regime-recognition-based condition-based maintenance programs involve a regime recognition step and a damage estimation step in which damage is calculated based on the identified regimes. Recently, new probabilistic regime recognition algorithms have been developed that produce probability distributions over the regimes, rather than deterministic regime classifications. However, to date, there has been no method to convert regime distributions to damage estimates. This paper proposes a technique to compute a probability distribution over the fatigue damage for each life-limited component directly from the regime probability distributions. The method treats the incurred damage at a given time as a random variable and accumulates the total damage incurred as a sum of random variables. The damage distribution at each time is computed from the regime distribution and the regime damage rates. A primary advantage of the approach is that it captures uncertainty in the regime recognition process by treating damage as a random variable rather than a deterministic value. Simulation results illustrate the benefit of the probabilistic approach over a deterministic method, particularly for flights where there is significant uncertainty in the flown regimes.

关键词： Rotorcrafts Probability Density Functions Helicopter Blade Flight Data interacting multiple model Computing Cumulative Damage Blade Element Theory Pilot Monte Carlo Simulation

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