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
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
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
In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algo...In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algorithm.In the proposed M-IMM algorithm,a new likelihood function is defined for the sake of updating flight mode probabilities,in which the influences of interacting to residual's mean error are taken into account and the assumption of likelihood function being a zero mean Gaussian function is discarded.Finally,the proposed M-IMM algorithm is applied to the simulation of the aircraft trajectory prediction,and the comparative studies are conducted to existing algorithms.The simulation results indicate the proposed M-IMM algorithm can predict aircraft trajectory more quickly and accurately.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
针对跟踪复杂机动目标过程中由于目标运动状态发生变化导致的跟踪误差较大的问题,提出一种自适应交互多模型无迹卡尔曼滤波(interacting multiple model unscented Kalman filter,IMM-UKF)算法,使用模型概率后验信息和模型似然函数自适...针对跟踪复杂机动目标过程中由于目标运动状态发生变化导致的跟踪误差较大的问题,提出一种自适应交互多模型无迹卡尔曼滤波(interacting multiple model unscented Kalman filter,IMM-UKF)算法,使用模型概率后验信息和模型似然函数自适应修正马尔可夫转移概率矩阵(transition probability matrix,TPM)。设计模型概率校正方法和模型转移加速方法,两种方法分别作用于模型稳定阶段和模型转移阶段,提高模型概率准确度和模型转移响应速度,减小状态估计误差。最后,通过两种场景下的实验验证所提算法在目标具有复杂运动状态下的性能,并与传统方法进行对比分析,在目标做机动运动时,位置精度和速度精度分别提高了15%和26%,验证了算法的有效性和可行性。展开更多
In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the PO...In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the POMDP framework. The elements of the POMDP model are analyzed and described. The state transfer law in the model can be described by the method of interactive multiple model (IMM) due to the diversity of the target motion law, which is used to switch the motion model to accommodate target maneuvers, and hence improving the tracking accuracy. The simulation results show that the model can achieve efficient planning for the UAV route, and effective tracking for the target. Furthermore, the path planned by this model is more reasonable and efficient than that by using the single state transition law.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
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
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.
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Foundation item： Supported by the National Nature Science Foundation of China （No. 61074053, 61374114） and the Applied Basic Research Program of Ministry of Transport of China （No. 2011-329-225 -390）.
文摘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.
基金National Natural Science Foundation of China(No.71401072)Natural Science Foundation of Jiangsu Province,China(No.BK20130814)Fundamental Research Funds for the Central Universities,China(No.NS2013064)
文摘In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algorithm.In the proposed M-IMM algorithm,a new likelihood function is defined for the sake of updating flight mode probabilities,in which the influences of interacting to residual's mean error are taken into account and the assumption of likelihood function being a zero mean Gaussian function is discarded.Finally,the proposed M-IMM algorithm is applied to the simulation of the aircraft trajectory prediction,and the comparative studies are conducted to existing algorithms.The simulation results indicate the proposed M-IMM algorithm can predict aircraft trajectory more quickly and accurately.
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.
文摘针对跟踪复杂机动目标过程中由于目标运动状态发生变化导致的跟踪误差较大的问题,提出一种自适应交互多模型无迹卡尔曼滤波(interacting multiple model unscented Kalman filter,IMM-UKF)算法,使用模型概率后验信息和模型似然函数自适应修正马尔可夫转移概率矩阵(transition probability matrix,TPM)。设计模型概率校正方法和模型转移加速方法,两种方法分别作用于模型稳定阶段和模型转移阶段,提高模型概率准确度和模型转移响应速度,减小状态估计误差。最后,通过两种场景下的实验验证所提算法在目标具有复杂运动状态下的性能,并与传统方法进行对比分析,在目标做机动运动时,位置精度和速度精度分别提高了15%和26%,验证了算法的有效性和可行性。
基金supported by the Aeronautical Science Foundation of China(20135153031 20135553035 2017ZC53033)
文摘In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the POMDP framework. The elements of the POMDP model are analyzed and described. The state transfer law in the model can be described by the method of interactive multiple model (IMM) due to the diversity of the target motion law, which is used to switch the motion model to accommodate target maneuvers, and hence improving the tracking accuracy. The simulation results show that the model can achieve efficient planning for the UAV route, and effective tracking for the target. Furthermore, the path planned by this model is more reasonable and efficient than that by using the single state transition law.
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 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.
