Maneuvering target track-before-detect via multiple-model Bernoulli particle filter

2015 ◽  
Vol 22 (10) ◽  
pp. 3935-3945 ◽  
Author(s):  
Rong-hui Zhan ◽  
Sheng-qi Liu ◽  
Jie-min Hu ◽  
Jun Zhang
Keyword(s):  
Particle Filter ◽  
Multiple Model ◽  
Maneuvering Target ◽  
Target Track ◽  
Track Before Detect

Multiple Model Particle Filter Based Track-before-Detect for Maneuvering Weak Target

2011 ◽  
Vol 30 (4) ◽  
pp. 941-944 ◽  
Author(s):  
Ya-xin Gong ◽  
Hong-wen Yang ◽  
Wei-dong Hu ◽  
Wen-xian Yu
Keyword(s):  
Particle Filter ◽  
Multiple Model ◽  
Model Particle ◽  
Track Before Detect ◽  
Weak Target

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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