A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks (such as, particle impoverishment and numerical sensitivity in calculating the prior) of the conv...A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks (such as, particle impoverishment and numerical sensitivity in calculating the prior) of the conventional unscented particle filter (UPF) confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution, Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.展开更多
Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a...Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a new estimating method is proposed.First the nonlinear vehicle dynamics system,containing inaccurate model parameters and constant noise,is established.Then a dual unscented particle filter(DUPF)algorithm is proposed.In the algorithm two unscented particle filters run in parallel,states estimation and parameters estimation update each other.The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter(UKF)and dual extended Kalman filter(DEKF),and it also has good capability to revise model parameters.展开更多
Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles...Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.展开更多
In order to track ground moving target, a variable structure interacting multiple model (VS-IMM) using mean shift unscented particle filter (MS-UPF) is proposed in this paper. In model-conditioned filtering, sampl...In order to track ground moving target, a variable structure interacting multiple model (VS-IMM) using mean shift unscented particle filter (MS-UPF) is proposed in this paper. In model-conditioned filtering, sample particles obtained from the unscented particle filter are moved towards the maximal posterior density estimation of the target state through mean shift. On the basis of stop model in VS-IMM, hide model is proposed. Once the target is obscured by terrain, the prediction at prior time is used instead of the measurement at posterior time; in addition, the road model set used is not changed. A ground moving target indication (GMTI) radar is employed in three common simulation scenarios of ground target: entering or leaving a road, crossing a junction and no measurement. Two evaluation indexes, root mean square error (RMSE) and average normalized estimation error squared (ANEES), are used. The results indicate that when the road on which the target moving changes, the tracking accuracy is effectively improved in the proposed algorithm. Moreover, track interruption could be avoided if the target is moving too slowly or masked by terrain.展开更多
Many Bayesian learning approaches to the multi-layer perceptron (MLP) parameter optimization have been proposed such as the extended Kalman filter (EKF). This paper uses the unscented Kalman particle filter (UPF...Many Bayesian learning approaches to the multi-layer perceptron (MLP) parameter optimization have been proposed such as the extended Kalman filter (EKF). This paper uses the unscented Kalman particle filter (UPF) to train the MLP in a self- organizing state space (SOSS) model. This involves forming augmented state vectors consisting of all parameters (the weights of the MLP) and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.展开更多
This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the ...This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test.展开更多
A novel multi-baseline phase unwrapping algorithm based on the unscented particle filter for interferometric synthetic aperture radar (INSAR) technology application is proposed. The proposed method is not constraine...A novel multi-baseline phase unwrapping algorithm based on the unscented particle filter for interferometric synthetic aperture radar (INSAR) technology application is proposed. The proposed method is not constrained by the nonlinearity of the problem and is independent of noise statistics, and performs noise eliminating and phase unwrapping at the same time by combining with an unscented particle filter with a path-following strategy and an omni-directional local phase slope estimator. Results obtained from multi-baseline synthetic data and single-baseline real data show the performance of the proposed method.展开更多
FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem(SLAM). However, in this framework there are two important potential limitatio...FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem(SLAM). However, in this framework there are two important potential limitations, the particle depletion problem and the linear approximations of the nonlinear functions. To overcome these two drawbacks, this paper proposes a new FastSLAM algorithm based on revised genetic resampling and square root unscented particle filter(SR-UPF). Double roulette wheels as the selection operator, and fast Metropolis-Hastings(MH) as the mutation operator and traditional crossover are combined to form a new resampling method. Amending the particle degeneracy and keeping the particle diversity are both taken into considerations in this method. As SR-UPF propagates the sigma points through the true nonlinearity, it decreases the linearization errors. By directly transferring the square root of the state covariance matrix, SR-UPF has better numerical stability. Both simulation and experimental results demonstrate that the proposed algorithm can improve the diversity of particles, and perform well on estimation accuracy and consistency.展开更多
文摘A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks (such as, particle impoverishment and numerical sensitivity in calculating the prior) of the conventional unscented particle filter (UPF) confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution, Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.
基金Supported by the National Natural Science Foundation of China(10902049)the Chinese Postdoctoral Science Foundation(2012M521073)+3 种基金the Fundamental Research Funds for the Central Universitiesthe Jiangsu Planned Projects for Postdoctoral Research Funds(1302020C)the Nanjing University of Aeronautics and Astronautics Student Innovative Training Program(20120119101535)the Fundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics(kfjj201404)
文摘Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a new estimating method is proposed.First the nonlinear vehicle dynamics system,containing inaccurate model parameters and constant noise,is established.Then a dual unscented particle filter(DUPF)algorithm is proposed.In the algorithm two unscented particle filters run in parallel,states estimation and parameters estimation update each other.The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter(UKF)and dual extended Kalman filter(DEKF),and it also has good capability to revise model parameters.
基金Supported by National Key Research and Development Program of China(Grant No.2021YFB2500703)Science and Technology Department Program of Jilin Province of China(Grant No.20230101121JC).
文摘Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.
文摘In order to track ground moving target, a variable structure interacting multiple model (VS-IMM) using mean shift unscented particle filter (MS-UPF) is proposed in this paper. In model-conditioned filtering, sample particles obtained from the unscented particle filter are moved towards the maximal posterior density estimation of the target state through mean shift. On the basis of stop model in VS-IMM, hide model is proposed. Once the target is obscured by terrain, the prediction at prior time is used instead of the measurement at posterior time; in addition, the road model set used is not changed. A ground moving target indication (GMTI) radar is employed in three common simulation scenarios of ground target: entering or leaving a road, crossing a junction and no measurement. Two evaluation indexes, root mean square error (RMSE) and average normalized estimation error squared (ANEES), are used. The results indicate that when the road on which the target moving changes, the tracking accuracy is effectively improved in the proposed algorithm. Moreover, track interruption could be avoided if the target is moving too slowly or masked by terrain.
基金supported by the National Natural Science Foundation of China(7092100160574058)+1 种基金the Key International Cooperation Programs of Hunan Provincial Science & Technology Department (2009WK2009)the General Program of Hunan Provincial Education Department(11C0023)
文摘Many Bayesian learning approaches to the multi-layer perceptron (MLP) parameter optimization have been proposed such as the extended Kalman filter (EKF). This paper uses the unscented Kalman particle filter (UPF) to train the MLP in a self- organizing state space (SOSS) model. This involves forming augmented state vectors consisting of all parameters (the weights of the MLP) and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.
基金The MKE(the Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2012-H0301-12-2006)
文摘This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test.
基金supported by the National Natural Science Foundation of China(41201479)the Scientific Research Project of Guilin University of Electronic Technology(UF11015Y)
文摘A novel multi-baseline phase unwrapping algorithm based on the unscented particle filter for interferometric synthetic aperture radar (INSAR) technology application is proposed. The proposed method is not constrained by the nonlinearity of the problem and is independent of noise statistics, and performs noise eliminating and phase unwrapping at the same time by combining with an unscented particle filter with a path-following strategy and an omni-directional local phase slope estimator. Results obtained from multi-baseline synthetic data and single-baseline real data show the performance of the proposed method.
基金supported by National Natural Science Foundation of China(No.61101197)Research Fund for the Doctoral Program of Higher Education of China(No.20093219120025)
文摘FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem(SLAM). However, in this framework there are two important potential limitations, the particle depletion problem and the linear approximations of the nonlinear functions. To overcome these two drawbacks, this paper proposes a new FastSLAM algorithm based on revised genetic resampling and square root unscented particle filter(SR-UPF). Double roulette wheels as the selection operator, and fast Metropolis-Hastings(MH) as the mutation operator and traditional crossover are combined to form a new resampling method. Amending the particle degeneracy and keeping the particle diversity are both taken into considerations in this method. As SR-UPF propagates the sigma points through the true nonlinearity, it decreases the linearization errors. By directly transferring the square root of the state covariance matrix, SR-UPF has better numerical stability. Both simulation and experimental results demonstrate that the proposed algorithm can improve the diversity of particles, and perform well on estimation accuracy and consistency.