An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

2013 ◽  
Vol 23 (5) ◽  
pp. 1678-1685 ◽  
Author(s):  
Ahmadreza Amirzadeh ◽  
Ali Karimpour
Keyword(s):  
Target Tracking ◽  
Kalman Filtering ◽  
Fading Memory ◽  
Maneuvering Target Tracking ◽  
Filtering Method ◽  
Maneuvering Target

High maneuvering target tracking using fuzzy fading memory

2009 ◽  
Author(s):  
Mohamad Hasan Bahari ◽  
Asad Azemi ◽  
Naser Pariz ◽  
Said Khorashadi Zadeh ◽  
Seyed Mohsen Davarpanah
Keyword(s):  
Target Tracking ◽  
Fading Memory ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target

Algorithm for Maneuvering Target Tracking Based on MATLAB

2013 ◽  
Vol 427-429 ◽  
pp. 1585-1588
Author(s):  
Jiang Ming Kuang ◽  
Shuang Zhang
Keyword(s):  
Nonlinear System ◽  
Target Tracking ◽  
Kalman Filtering ◽  
Tracking Error ◽  
National Defense ◽  
Extended Kalman Filtering ◽  
Maneuvering Target Tracking ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Stable Performance

the theory for maneuvering target tracking is significant to national defense and civil application. The filtering algorithm is one of important components in maneuvering target tracking. After the model of the maneuvering target is built, state vectors in the model are forecast and estimated through relevant filtering algorithms. The Unscented Kalman filtering is a novel filtering algorithm specially used for the nonlinear system, which is characterized by easy implementation, good generality, stable performance and so forth. Compared with the traditional Extended Kalman Filtering algorithm, the filtering algorithm can achieve less tracking error and higher tracking precision.

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