Federated unscented particle filtering algorithm for SINS/CNS/GPS system

To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

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.

Modified unscented particle filter for nonlinear Bayesian tracking

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.

An unscented particle filter for ground maneuvering target tracking

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.

Vehicle State and Parameter Estimation Based on Dual Unscented Particle Filter Algorithm

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.

MLP training in a self-organizing state space model using unscented Kalman particle filter

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.

Unscented Particle Filter Algorithm for Ballistic Target Tracking

At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was carried out from the aspects such as particle degradation and particle diversity lack. A novel ballistic coefficient parameter model was built, and was expanded to the state vector for filtering. Finally, the improved algorithm was simulated by MATLAB software. The simulation results show that the algorithm can obtain better convergence speed and tracking precision.

Particle Filter for Estimation and Tracking

In recent years, the particle filter technique has been widely used in tracking, estimation and navigation. In this paper, the authors described several practical filters including the general practical, the extended Kaman practical, and the unsented particle filters. And they explained the degeneracy problem in the practical filter protess, and introduced some solved methods. Finally they demonstrated the estimation of different particle filters in non-liner and non-Gaussian situation respectively. The result proved the unscented particle filter had the best performance.

Ground Moving Target Tracking with VS-IMM Using Mean Shift Unscented Particle Filter

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.

UPF based autonomous navigation scheme for deep space probe

The autonomous "celestial navigation scheme" for deep space probe departing from the earth and the autonomous "optical navigation scheme" for encountering object celestial body are presented. Then, aiming at the conditions that large initial estimation errors and non-Gaussian distribution of state or measurement errors may exist in orbit determination process of the two phases, UPF (unscented particle filter) is introduced into the navigation schemes. By tackling nonlinear and non-Gaussian problems, UPF overcomes the accuracy influence brought by the traditional EKF (extended Kalman filter), UKF (unscented Kalman filter), and PF (particle filter) schemes in approximate treatment to nonlinear and non-Gaussian state model and measurement model. The numerical simulations demonstrate the feasibility and higher accuracy of the UPF navigation scheme.

Novel algorithm for geomagnetic navigation

To solve the highly nonlinear and non-Gaussian recursive state estimation problem in geomagnetic navigation, the unscented particle filter （UPF） was introduced to navigation system. The simulation indicates that geomagnetic navigation using UPF could complete the position estimation with large initial horizontal position errors. However, this navigation system could only provide the position information. To provide all the kinematics states estimation of aircraft, a novel autonomous navigation algorithm, named unscented particle and Kalman hybrid navigation algorithm （UPKHNA）, was proposed for geomagnetic navigation, The UPKHNA used the output of UPF and barometric altimeter as position measurement, and employed the Kahnan filter to estimate the kinematics states of aircraft. The simulation shows that geomagnetic navigation using UPKHNA could provide all the kinematics states estimation of aircraft continuously, and the horizontal positioning performance is better than that only using the UPF.

Outdoor position estimation based on a combination system of GPS-INS by using UPF

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.

Multi-baseline phase unwrapping algorithm for INSAR

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.

An Improved FastSLAM Algorithm Based on Revised Genetic Resampling and SR-UPF

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.