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3D Human Motion Tracking by Using Interactive Multiple Models 被引量:1
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作者 仝明磊 边后琴 《Journal of Shanghai Jiaotong university(Science)》 EI 2011年第4期420-428,共9页
Of different model-based methods in vision based human tracking,many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution a... Of different model-based methods in vision based human tracking,many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution according to a proper likelihood function.Seldom works perform a framework of interactive multiple models (IMM) to track a human for challenging problems,such as uncertainty of motion styles,imprecise detection of feature points and ambiguity of joint location.This paper presents a two-layer filter framework based on IMM to track human motion.First,a method of model based points location is proposed to detect key feature points automatically and the filter in the first layer is performed to estimate the undetected points.Second,multiple models of motion are learned by the prior motion data with ridge regression and the IMM algorithm is used to estimate the quaternion vectors of joints rotation.Finally,experiments using real images sequences,simulation videos and 3D voxel data demonstrate that this human tracking framework is efficient. 展开更多
关键词 interactive multiple models(IMM) human tracking automatic location occlusion prediction
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Target Tracking Using the Interactive Multiple Model Method 被引量:6
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作者 张劲松 杨位钦 胡士强 《Journal of Beijing Institute of Technology》 EI CAS 1998年第3期299-304,共6页
Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the of... Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method. 展开更多
关键词 interactive multiple model TRACKING maneuvering target Kalman filter
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An interacting multiple model-based two-stage Kalman filter for vehicle positioning 被引量:2
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作者 徐启敏 李旭 +1 位作者 李斌 宋向辉 《Journal of Southeast University(English Edition)》 EI CAS 2017年第2期177-181,共5页
To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system(... To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system(MEMS) inertial sensors, a novel interacting multiple model-based two-stage Kalman filter(IMM-TSKF) is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF. 展开更多
关键词 interacting multiple model(IMM) two-stage filter uncertain noise vehicle positioning
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Maneuvering target tracking using threshold interacting multiple model algorithm
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作者 徐迈 山秀明 徐保国 《Journal of Southeast University(English Edition)》 EI CAS 2005年第4期440-444,共5页
To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model (TIMM) is proposed. This algorithm i... To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model (TIMM) is proposed. This algorithm is based on the interacting multiple model (IMM) method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square (RMS) errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy. 展开更多
关键词 maneuvering target tracking Kalman filter interacting multiple model (IMM) threshold interacting multiple model (TIMM)
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Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler 被引量:16
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作者 DU Shi-chuan SHI Zhi-guo +1 位作者 ZANG Wei CHEN Kang-sheng 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2007年第8期1277-1282,共6页
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' intent... 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. 展开更多
关键词 Interacting multiple model Particle filter Blind Doppler
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Interacting Multiple Model Algorithm with the Unscented Particle Filter (UPF) 被引量:8
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作者 邓小龙 谢剑英 倪宏伟 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2005年第4期366-371,共6页
Combining interacting multiple model (IMM) and unscented particle filter (UPF), a new multiple model filtering algorithm is presented. Multiple models can be adapted to targets' high maneu- vering. Particle filte... Combining interacting multiple model (IMM) and unscented particle filter (UPF), a new multiple model filtering algorithm is presented. Multiple models can be adapted to targets' high maneu- vering. Particle filter can be used to deal with the nonlinear or non-Gaussian problems and the unscented Kalman filter (UKF) can improve the approximate accuracy. Compared with other interacting multiple model algorithms in the simulations, the results demonstrate the validity of the new filtering method. 展开更多
关键词 interacting multiple model UPF UKF nonlinear/non-Gaussian
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Method for Underwater Target Tracking Based on an Interacting Multiple Model 被引量:6
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作者 XU Weiming LIU Yanchun YIN Xiaodong 《Geo-Spatial Information Science》 2008年第3期186-190,共5页
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 ... 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. 展开更多
关键词 underwater target TRACKING interacting multiple model fuzzy logic inference
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An Algorithm of the Adaptive Grid and Fuzzy Interacting Multiple Model 被引量:3
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作者 Yuan Zhang Chen Guo +2 位作者 Hai Hu Shubo Liu Junbo Chu 《Journal of Marine Science and Application》 2014年第3期340-345,共6页
This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model (AGFIMM) for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model (FSMM) algo... This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model (AGFIMM) for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model (FSMM) algorithm's cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model (IMM) algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for enineering apolications. 展开更多
关键词 maneuvering target tracking adaptive grid fuzzy logicinference variable structure multiple model adaptive grid andfuzzy interacting multiple model (AGFIMM) interacting multiplemodel (IMM)
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Multiple Model Filtering in the Presence of Gaussian Mixture Measurement Noises 被引量:1
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作者 张永安 周荻 段广仁 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2004年第4期229-234,共6页
A simplified multiple model filter is developed for discrete-time systems inthe presence of Gaussian mixture measurement noises. Theoretical analysis proves that the proposedfilter has the same estimation performance ... A simplified multiple model filter is developed for discrete-time systems inthe presence of Gaussian mixture measurement noises. Theoretical analysis proves that the proposedfilter has the same estimation performance as the interacting multiple model filter at the price ofless computational cost. Numerically robust implementation of the filter is presented to meetpractical applications. An example on bearings-only guidance demonstrates the effect of the proposedalgorithm. 展开更多
关键词 state estimation multiple model filter interacting multiple model Gaussianmixture target tracking bearings-only guidance
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A novel maneuvering multi-target tracking algorithm based on multiple model particle filter in clutters 被引量:2
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作者 胡振涛 Pan Quan Yang Feng 《High Technology Letters》 EI CAS 2011年第1期19-24,共6页
To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle fi... To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle filter is presented in this paper. The algorithm realizes dynamic combination of multiple model particle filter and joint probabilistic data association algorithm. The rapid expan- sion of computational complexity, caused by the simple combination of the interacting multiple model algorithm and particle filter is solved by introducing model information into the sampling process of particle state, and the effective validation and utilization of echo is accomplished by the joint proba- bilistic data association algorithm. The concrete steps of the algorithm are given, and the theory analysis and simulation results show the validity of the method. 展开更多
关键词 maneuvering multi-target tracking multiple model particle filter interacting multiple model IMM) joint probabilistic data association
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ADAPTIVE MULTIPLE MODEL FILTER USING IMM AND STF
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作者 梁彦 潘泉 +1 位作者 周东华 张洪才 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2000年第3期-,共5页
In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching th... In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM. 展开更多
关键词 tracking maneuvering targets interacting multiple model adaptive filtering Kalman filtering strong tracking filter
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Application of interacting multiple model in integrated positioning system of vehicle
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作者 WEI Wen jun GAO Xue ze +1 位作者 GE Li rain GAO Zhong jun 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2018年第3期279-285,共7页
To solve low precision and poor stability of the extended Kalman filter (EKF) in the vehicle integrated positioning system owing to acceleration, deceleration and turning (hereinafter referred to as maneuvering) ,... To solve low precision and poor stability of the extended Kalman filter (EKF) in the vehicle integrated positioning system owing to acceleration, deceleration and turning (hereinafter referred to as maneuvering) , the paper presents an adaptive filter algorithm that combines interacting multiple model (IMM) and non linear Kalman filter. The algorithm describes the motion mode of vehicle by using three state spacemode]s. At first, the parallel filter of each model is realized by using multiple nonlinear filters. Then the weight integration of filtering result is carried out by using the model matching likelihood function so as to get the system positioning information. The method has advantages of nonlinear system filter and overcomes disadvantages of single model of filtering algorithm that has poor effects on positioning the maneuvering target. At last, the paper uses IMM and EKF methods to simulate the global positioning system (OPS)/inertial navigation system (INS)/dead reckoning (DR) integrated positioning system, respectively. The results indicate that the IMM algorithm is obviously superior to EKF filter used in the integrated positioning system at present. Moreover, it can greatly enhance the stability and positioning precision of integrated positioning system. 展开更多
关键词 VEHICLE integrated positioning system information fusion algorithm extended Kalman filter (KEF) interacting multiple model (IMM)
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An unscented particle filter for ground maneuvering target tracking 被引量:6
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作者 GUO Rong-hua QIN Zheng 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2007年第10期1588-1595,共8页
In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unsc... In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but an UPF is adopted in each model. Therefore, the filtering performance and degeneracy phenomenon of particles are improved. The filtering method addresses nonlinear and/or non-Gaussian tracking problems. Simulation results show that the method has better tracking performance compared with the standard IMM-type filter and IMM particle filter. 展开更多
关键词 Interacting multiple model (IMM) Unscented particle filter (UPF) Ground target tracking Particle filter (PF)
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Novel sensor selection strategy for LPI based on an improved IMMPF tracking method 被引量:5
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作者 Zhenkai Zhang Jiehao Zhu +1 位作者 Yubo Tian Hailin Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第6期1004-1010,共7页
Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent perfo... Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept(LPI). In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter(IMMPF) tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target's maneuverability and the state's uncertainty to decide the radar's radiation time. The radar will work only when the target's maneuverability and the state's uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations. 展开更多
关键词 sensor selection low probability of intercept(LPI) interacting multiple model(IMM) particle filter target tracking
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A solution of UAV localization problem using an interacting multiple nonlinear fuzzy adaptive H_(∞)models filter algorithm 被引量:3
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作者 Elzoghby MOSTAFA Li FU +1 位作者 Arafa IBRAHIM.I. Arif USMAN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2019年第4期978-990,共13页
The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigat... The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigation scheme based on Interacting Multiple Nonlinear Fuzzy Adaptive H_∞ Models(IMM-NFAH_∞) filtering technique for UAV is presented. The proposed IMM-NFAH_∞ strategy switches between two different Nonlinear Fuzzy Adaptive H_∞(NFAH_∞) filters and each NFAH_∞ filter is based on different fuzzy logic inference systems. The newly proposed technique takes into consideration the high order Taylor series terms and adapts the nonlinear H_∞ filter based on different fuzzy inference systems via adaptive filter bounds(di),along with disturbance attenuation parameter c. Simulation analysis validates the performance of the proposed algorithm, and the comparison with nonlinear H_∞(NH_∞) filter and that with different NFAH_∞ filters demonstrate the effectiveness of UAV localization utilizing IMM-NFAH_∞ filter. 展开更多
关键词 Interacting multiple models Integrated navigation system Multi-mode estimation Nonlinear fuzzy adaptive filter Sensor data fusion UAV localization
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Application of Road Information in Ground Moving Target Tracking 被引量:3
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作者 Zhen Xinyan Zhao Wei 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2007年第6期529-538,共10页
A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dyn... A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification (DMM VS-IMM for short). Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads. 展开更多
关键词 ground moving target tracking state estimation road information models modification variable structure interacting multiple model (VS-IMM)
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Modified switched IMM estimator based on autoregressive extended Viterbi method for maneuvering target tracking 被引量:3
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作者 HADAEGH Mahmoudreza KHALOOZADEH Hamid 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第6期1142-1157,共16页
In this paper, a new approach of maneuvering target tracking algorithm based on the autoregressive extended Viterbi(AREV) model is proposed. In contrast to weakness of traditional constant velocity(CV) and constant ac... In this paper, a new approach of maneuvering target tracking algorithm based on the autoregressive extended Viterbi(AREV) model is proposed. In contrast to weakness of traditional constant velocity(CV) and constant acceleration(CA) models to noise effect reduction, the autoregressive(AR) part of the new model which changes the structure of state space equations is proposed. Also using a dynamic form of the state transition matrix leads to improving the rate of convergence and decreasing the noise effects. Since AR will impose the load of overmodeling to the computations, the extended Viterbi(EV) method is incorporated to AR in two cases of EV1 and EV2. According to most probable paths in the interacting multiple model(IMM) during nonmaneuvering and maneuvering parts of estimation, EV1 and EV2 respectively can decrease load of overmodeling computations and improve the AR performance. This new method is coupled with proposed detection schemes for maneuver occurrence and termination as well as for switching initializations. Appropriate design parameter values are derived for the detection schemes of maneuver occurrences and terminations. Finally, simulations demonstrate that the performance of the proposed model is better than the other older linear and also nonlinear algorithms in constant velocity motions and also in various types of maneuvers. 展开更多
关键词 interacting multiple model(IMM) filter constant acceleration(CA) autoregressive(AR) extended Viterbi(EV) autoregressive extended Viterbi(AREV) extended Kalman filter(EKF)
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Tracking a maneuvering target in clutter with out-of-sequence measurements for airborne radar 被引量:3
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作者 Weihua Wu Jing Jiang Yang Wan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第4期746-753,共8页
There are many proposed optimal or suboptimal al- gorithms to update out-of-sequence measurement(s) (OoSM(s)) for linear-Gaussian systems, but few algorithms are dedicated to track a maneuvering target in clutte... There are many proposed optimal or suboptimal al- gorithms to update out-of-sequence measurement(s) (OoSM(s)) for linear-Gaussian systems, but few algorithms are dedicated to track a maneuvering target in clutter by using OoSMs. In order to address the nonlinear OoSMs obtained by the airborne radar located on a moving platform from a maneuvering target in clut- ter, an interacting multiple model probabilistic data association (IMMPDA) algorithm with the OoSM is developed. To be practical, the algorithm is based on the Earth-centered Earth-fixed (ECEF) coordinate system where it considers the effect of the platform's attitude and the curvature of the Earth. The proposed method is validated through the Monte Carlo test compared with the perfor- mance of the standard IMMPDA algorithm ignoring the OoSM, and the conclusions show that using the OoSM can improve the track- ing performance, and the shorter the lag step is, the greater degree the performance is improved, but when the lag step is large, the performance is not improved any more by using the OoSM, which can provide some references for engineering application. 展开更多
关键词 out-of-sequence measurement(s) (OoSM(s)) Earth-centered Earth-fixed (ECEF) interacting multiple model (IMM) probabilistic data association (PDA) attitude.
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Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update 被引量:4
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作者 胡振涛 Fu Chunling +1 位作者 Cao Zhiwei Li Congcong 《High Technology Letters》 EI CAS 2015年第1期39-45,共7页
Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with it... Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is proposed.Firstly,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation update.Secondly,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering step.In the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm. 展开更多
关键词 maneuvering target tracking nonlinear filtering cubature Kalman filter(CKF) interacting multiple model(IMM)
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Maneuvering target state estimation based on separate model-ing of target trajectory shape and dynamic characteristics 被引量:2
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作者 ZHANG Zhuanhua ZHOU Gongjian 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2022年第5期1195-1209,共15页
The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a ta... The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers. 展开更多
关键词 maneuvering target tracking separate modeling natural parametric function interacting multiple model(IMM)filter data fitting state augmentation
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