Multirate Interacting Multiple Model Algorithm Combined with Particle Filter for Nonlinear/Non-Gaussian Target Tracking

2006 ◽  
Author(s):  
Guixi Liu ◽  
Enke Gao ◽  
Chunyu Fan
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Non Gaussian ◽  
Model Algorithm

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 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.

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