Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking

2014 ◽  
Vol 25 (3) ◽  
pp. 380-385 ◽  
Author(s):  
Changyun Liu ◽  
Penglang Shui ◽  
Gang Wei ◽  
Song Li
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Scale Factor ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target

A Maneuvering Target Tracking Algorithm Based on Gaussian Filter for Multiple Passive Sensors

2011 ◽  
Vol 467-469 ◽  
pp. 447-452
Author(s):  
Jin Long Yang ◽  
Hong Bing Ji ◽  
Jin Mang Liu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Nonlinear Problem ◽  
Unscented Kalman Filter ◽  
Tracking Algorithm ◽  
Gaussian Filter ◽  
Maneuvering Target Tracking ◽  
Selection Scheme ◽  
Maneuvering Target ◽  
Passive Sensors

When tracking a maneuvering target by multiple passive sensors, two problems need to be considered, one is the nonlinear problem, another is the maneuvering problem. Taking these into account, a Gaussian filter (GF) for nonlinear Bayesian estimation is introduced based on a deterministic sample selection scheme, which can solve the nonlinear problem better than the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Then, a new maneuvering target tracking algorithm is proposed based on the GF and Interacting Multiple Mode (IMM), called IMM-GF method in this paper. Simulation results show that the proposed method has better performance than the IMM-EKF and IMM-UKF in tracking a maneuvering target for multiple passive sensors.

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