scholarly journals IMM Iterated Extended Particle Filter Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yang Wan ◽  
Shouyong Wang ◽  
Xing Qin
Keyword(s):  
Particle Filter ◽  
Density Function ◽  
The State ◽  
Arbitrary Distribution ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Importance Density Function

In order to solve the tracking problem of radar maneuvering target in nonlinear system model and non-Gaussian noise background, this paper puts forward one interacting multiple model (IMM) iterated extended particle filter algorithm (IMM-IEHPF). The algorithm makes use of multiple modes to model the target motion form to track any maneuvering target and each mode uses iterated extended particle filter (IEHPF) to deal with the state estimation problem of nonlinear non-Gaussian system. IEHPF is an improved particle filter algorithm, which utilizes iterated extended filter (IEHF) to obtain the mean value and covariance of each particle and describes importance density function as a combination of Gaussian distribution. Then according to the function, draw particles to approximate the state posteriori density of each mode. Due to the high filter accuracy of IEHF and the adaptation of system noise with arbitrary distribution as well as strong robustness, the importance density function generated by this method is more approximate to the true sate posteriori density. Finally, a numerical example is included to illustrate the effectiveness of the proposed methods.

ALGORITHM OF IMM COMBINING KALMAN AND PARTICLE FILTER FOR MANEUVERING TARGET TRACKING

2006 ◽  
Vol 03 (04) ◽  
pp. 321-328
Author(s):  
GUIXI LIU ◽  
ENKE GAO ◽  
CHUNYU FAN
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Nonlinear Problems ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Computation Efficiency ◽  
Non Gaussian

The particle filter can deal with nonlinear/non-Gaussian problems and it has been introduced to the algorithm of the interacting multiple model (IMM) for higher precision. The general IMM based on Kalman filter or extended Kalman filter (IMMEKF) cannot deal with non-Gaussian problems and also does not work as well as the IMM based on the particle filter (IMMPF) for the nonlinear problems. However the problem of the particle filter is its expensive computation, because a particle filter usually has a lot of particles, which will increase the computation load greatly. Here an algorithm of IMM combining the Kalman filter and the particle filter (IMMK-PF) for maneuvering target tracking is proposed to improve the computation efficiency as compared to the IMMPF. For nonlinear/Gaussian problems the new algorithm is expected to have a good performance as the IMMPF, while for linear problems it will perform similarly to the IMMEKF and will work better than the IMMPF.

scholarly journals Robust Interacting Multiple Model Unscented Particle Filter for Navigation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Li Xue ◽  
Yulan Han ◽  
Chunning Na
Keyword(s):  
Particle Filter ◽  
Navigation System ◽  
Measurement Information ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Filter Performance ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.

Fuzzy Interacting Multiple Model H$$\infty$$ Particle Filter Algorithm Based on Current Statistical Model

2019 ◽  
Vol 21 (6) ◽  
pp. 1894-1905
Author(s):  
Qicong Wang ◽  
Xiaoqiang Chen ◽  
Lin Zhang ◽  
Jin Li ◽  
Chong Zhao ◽  
...  
Keyword(s):  
Statistical Model ◽  
Particle Filter ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Particle Filter Algorithm

Adaptive Interacting Multiple Model Unscented Particle Filter Tracking Algorithm

2012 ◽  
Vol 190-191 ◽  
pp. 906-910 ◽  
Author(s):  
Hong Jiang Liu
Keyword(s):  
Probability Density Function ◽  
Particle Filter ◽  
Probability Density ◽  
Real Time ◽  
Density Function ◽  
Tracking System ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Unscented Particle Filter

In order to study the tracking problem of maneuvering image sequence target in complex environment with multi-sensor array, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The motion array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

scholarly journals Study on Maneuvering Target On-axis Tracking Algorithm of Modified Current Statistical Model

2018 ◽  
Vol 160 ◽  
pp. 02008
Author(s):  
Xiong Zhenkai ◽  
Li Fanying ◽  
Zhang Lei
Keyword(s):  
Kalman Filter ◽  
Statistical Model ◽  
Particle Filter ◽  
Density Function ◽  
Unscented Kalman Filter ◽  
Tracking System ◽  
Filter Performance ◽  
Maneuvering Target ◽  
Simulation Results ◽  
Importance Density Function

Aiming at the model adaptability and the filter precision on the maneuvering target on-axis tracking, The paper put forward a filter algorithm based on modified current statistical model. The algorithm can enhance the model adaptability to the weak and non-maneuvering maneuvering target. The method uses Unscented Kalman Filter to obtain the importance density function of each particle, improves the Particle Filter estimation performance.By applying the proposed algorithm to the on-axis tracking system, the simulation results demonstrate that algorithm can effectively improve filter performance and tracking precision.

Adaptive Interacting Multiple Model Unscented Particle Filter for Dynamic Acoustic Array

2013 ◽  
Vol 300-301 ◽  
pp. 407-413
Author(s):  
Ya Lei Liu ◽  
Xiao Hui Gu
Keyword(s):  
Probability Density Function ◽  
Particle Filter ◽  
Probability Density ◽  
Real Time ◽  
Density Function ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Unscented Particle Filter ◽  
Acoustic Array

Abstract. In order to improve the tracking accuracy of 3D dynamic acoustic array to 2D maneuvering target in colored noise environment, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The 3D motion acoustic array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

Sign in / Sign up

Export Citation Format

Share Document