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
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
Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune s...Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune system （BIS） according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS, and then designs the models of components and the architecture for a monitoring agent, a regulating agent, a killer agent, a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system, the multi-agent system （MAS）, the vaccine and the danger theory （DT）. Moreover a new immune multi-agent model using vaccine based on DT （IMMUVBDT） for the cooperative air-defense SoS is advanced. The immune response and immune mechanism of the CASoSSWF are analyzed. The model has a capability of memory, evolution, commendable dynamic environment adaptability and self-learning, and embodies adequately the cooperative air-defense mechanism for the CASoSSWF. Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.展开更多
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
This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorre...This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.展开更多
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
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
With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirement...With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.展开更多
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
An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple ...An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.展开更多
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
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
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
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
The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a nov...The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is proposed.The framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of IMM.Considering the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation noise.On that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion way.The object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical sensors.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
There is one problem existing in gyroscope signal processing,which is that single models can＇ t adapt to change of carrier maneuvering process.Since it is difficult to identify the angular motion state of gyroscope c...There is one problem existing in gyroscope signal processing,which is that single models can＇ t adapt to change of carrier maneuvering process.Since it is difficult to identify the angular motion state of gyroscope carriers,interacting multiple model （IMM） is employed here to solve the problem.The Kalman filter-based IMM （IMMKF） algorithm is explained in detail and its application in gyro signal processing is introduced.And with the help of the Singer model,the system model set of gyro outputs is constructed.In order to demonstrate the effectiveness of the proposed approach,static experiment and dynamic experiment are carried out respectively.Simulation analysis results indicate that the IMMKF algorithm is excellent in eliminating gyro drift errors,which could adapt to the change of carrier maneuvering process well.展开更多
Recently,lots of smoothing techniques have been presented for maneuvering target tracking.Interacting multiple model-probabilistic data association(IMM-PDA)fixed-lag smoothing algorithm provides an efficient solution ...Recently,lots of smoothing techniques have been presented for maneuvering target tracking.Interacting multiple model-probabilistic data association(IMM-PDA)fixed-lag smoothing algorithm provides an efficient solution to track a maneuvering target in a cluttered environment.Whereas,the smoothing lag of each model in a model set is a fixed constant in traditional algorithms.A new approach is developed in this paper.Although this method is still based on IMM-PDA approach to a state augmented system,it adopts different smoothing lag according to diverse degrees of complexity of each model.As a result,the application is more flexible and the computational load is reduced greatly.Some simulations were conducted to track a highly maneuvering target in a cluttered environment using two sensors.The results illustrate the superiority of the proposed algorithm over comparative schemes,both in accuracy of track estimation and the computational load.展开更多
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
For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents ...For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents a novel interactive multiple model(IMM)algorithm optimized for tracking maneuvering near space hypersonic gliding vehicles(NSHGV)with a fast adaptive sam-pling control logic.The algorithm utilizes the model probabilities to dynamically adjust the revisit time corresponding to NSHGV maneuvers,thus achieving a balance between tracking accuracy and resource consumption.Simulation results on typical NSHGV targets show that the proposed algo-rithm improves tracking accuracy and resource allocation efficiency compared to other conventional multiple model algorithms.展开更多
基金The National Natural Science Foundation of China(No.61273236)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1637),China Scholarship Council
文摘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.
文摘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.
文摘Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune system （BIS） according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS, and then designs the models of components and the architecture for a monitoring agent, a regulating agent, a killer agent, a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system, the multi-agent system （MAS）, the vaccine and the danger theory （DT）. Moreover a new immune multi-agent model using vaccine based on DT （IMMUVBDT） for the cooperative air-defense SoS is advanced. The immune response and immune mechanism of the CASoSSWF are analyzed. The model has a capability of memory, evolution, commendable dynamic environment adaptability and self-learning, and embodies adequately the cooperative air-defense mechanism for the CASoSSWF. Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.
基金supported by the Fundamental Research Funds for the Central Universities(NJ20140010)the Scientific Research Start-up Funding from Jiangsu University of Science and Technology+1 种基金the Scienceand Technology on Electronic Information Control Laboratory Projectthe Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘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.
基金the National Natural Science Foundation of China(No.61374160)the Shanghai Aerospace Science and Technology Innovation Fund(No.SAST201237)
文摘This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.
基金Project supported by the National Natural Science Foundation ofChina (No. 60673024)the National Basic Research Program(973) of China (No. 2004CB719400)
文摘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.
基金supported by the National Natural Science Foundation of China(61671181).
文摘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.
基金supported by the Foundation of Key Laboratory of Near-Surface。
文摘With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.
基金Supported by the National Nature Science Foundations of China(No.61300214,U1204611,61170243)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+3 种基金the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Basic and Frontier Technology Research Plan of Henan Province(No.132300410148)the Funding Scheme of Young Key Teacher of Henan Province Universitiesthe Key Project of Teaching Reform Research of Henan University(No.HDXJJG2013-07)
文摘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.
基金the University of Tabriz through a Grant scheme No.808.
文摘An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.
文摘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.
基金supported by the National Natural Science Foundation of China(61102168)
文摘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.
基金the Research Fund for the Young Teacher of Shanghai(No.Z-2009-12)the New Teacher Fund of Shanghai University of Electric Power (No.K-2010-16)
文摘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.
基金Supported by the National Natural Science Foundation of China （60634030）, the National Natural Science Foundation of China （60702066, 6097219） and the Natural Science Foundation of Henan Province （092300410158）.
文摘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.
基金Supported by the Postdoctoral Science Foundation of China(No.2014M551999)the Open Foundation of Key Laboratory of Spectral Imaging Technology of the Chinese Academy of Sciences(No.LSIT201711D)
文摘The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is proposed.The framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of IMM.Considering the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation noise.On that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion way.The object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical sensors.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
基金Supported by the National High Technology Research and Development Program of China(No.2012AA061101)the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information(Nanjing University of Science and Technology),Ministry of Education(No.3092013012205)
文摘There is one problem existing in gyroscope signal processing,which is that single models can＇ t adapt to change of carrier maneuvering process.Since it is difficult to identify the angular motion state of gyroscope carriers,interacting multiple model （IMM） is employed here to solve the problem.The Kalman filter-based IMM （IMMKF） algorithm is explained in detail and its application in gyro signal processing is introduced.And with the help of the Singer model,the system model set of gyro outputs is constructed.In order to demonstrate the effectiveness of the proposed approach,static experiment and dynamic experiment are carried out respectively.Simulation analysis results indicate that the IMMKF algorithm is excellent in eliminating gyro drift errors,which could adapt to the change of carrier maneuvering process well.
基金This work is supported by the Projects of the State Key Fundamental Research(No.2001CB309403)
文摘Recently,lots of smoothing techniques have been presented for maneuvering target tracking.Interacting multiple model-probabilistic data association(IMM-PDA)fixed-lag smoothing algorithm provides an efficient solution to track a maneuvering target in a cluttered environment.Whereas,the smoothing lag of each model in a model set is a fixed constant in traditional algorithms.A new approach is developed in this paper.Although this method is still based on IMM-PDA approach to a state augmented system,it adopts different smoothing lag according to diverse degrees of complexity of each model.As a result,the application is more flexible and the computational load is reduced greatly.Some simulations were conducted to track a highly maneuvering target in a cluttered environment using two sensors.The results illustrate the superiority of the proposed algorithm over comparative schemes,both in accuracy of track estimation and the computational load.
基金National Natural Science Foundation of China(No.61663020)Project of Education Department of Gansu Province(No.2016B-036)
文摘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.
文摘For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents a novel interactive multiple model(IMM)algorithm optimized for tracking maneuvering near space hypersonic gliding vehicles(NSHGV)with a fast adaptive sam-pling control logic.The algorithm utilizes the model probabilities to dynamically adjust the revisit time corresponding to NSHGV maneuvers,thus achieving a balance between tracking accuracy and resource consumption.Simulation results on typical NSHGV targets show that the proposed algo-rithm improves tracking accuracy and resource allocation efficiency compared to other conventional multiple model algorithms.
