Separated Maneuvering Target Tracking Algorithm Along with Three Cartesian Coordinates for Three-Dimension Radar

2013 ◽  
Vol 15 (6) ◽  
pp. 1809-1820 ◽  
Author(s):  
Shicang Zhang ◽  
Jianxun Li ◽  
Liangbin Wu ◽  
Yan Zhou
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Three Dimension ◽  
Maneuvering Target Tracking ◽  
Cartesian Coordinates ◽  
Maneuvering Target

scholarly journals Radar maneuvering target tracking algorithm based on human cognition mechanism

2019 ◽  
Vol 32 (7) ◽  
pp. 1695-1704
Author(s):  
Shuliang WANG ◽  
Daping BI ◽  
Huailin RUAN ◽  
Mingyang DU
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Human Cognition ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target

An Adaptive Tracking Algorithm of Maneuvering Target

2011 ◽  
Vol 383-390 ◽  
pp. 2179-2183
Author(s):  
Zhang Song Shi ◽  
Pi Xu Zhang ◽  
Rui Li ◽  
Shen Wang
Keyword(s):  
Parameter Identification ◽  
Target Tracking ◽  
Adaptive Filtering ◽  
Tracking Algorithm ◽  
Maneuvering Target Tracking ◽  
Adaptive Tracking ◽  
Maneuvering Target ◽  
Working Cycle ◽  
Actual Trajectory ◽  
Identification Model

In the existing process of maneuvering target tracking, the target’s actual trajectory can not be foreseen precisely. A commonly attempt is under the assumption that the target’s trajectory is prefixed. The prefixed model is usually not inosculated with the target’s actual trajectory, and the target’s tracking precision can not be guaranteed. In this paper a parameter identification model is presented. This model identifies the target’s trajectory dynamically, adapts to the variation of working cycle, and covers kinds of possible state of the maneuvering target. An adaptive filtering is then employed to analyze the tracking. We call it as a whole adaptive tracking in this paper. Simulations verify that this adaptive tracking algorithm effectively improves the tracking precision.

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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