scholarly journals Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 741 ◽  
Author(s):  
Hua Liu ◽  
Wen Wu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Cubature Kalman Filter ◽  
Spherical Simplex

scholarly journals A Novel Fifth-Degree Strong Tracking Cubature Kalman Filter for Two-Dimensional Maneuvering Target Tracking

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhaoming Li ◽  
Wenge Yang ◽  
Dan Ding
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Jacobian Matrix ◽  
Two Dimensional ◽  
Tracking Accuracy ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Cubature Kalman Filter ◽  
Weighted Integral ◽  
Fading Factor

A novel fifth-degree strong tracking cubature Kalman filter is put forward to improve the two-dimensional maneuvering target tracking accuracy. First, a new fifth-degree cubature rule, with only one point more than the theoretical lower bound, is used to approximate the intractable nonlinear Gaussian weighted integral in the nonlinear Kalman filtering framework, and a novel fifth-degree cubature Kalman filter is proposed. Then, the suboptimal fading factor is designed for the filter to adjust the filtering gain matrix online and force the residual sequences mutually orthogonal, thus improving the ability of the filter to track the mutation state, and the fifth-degree strong tracking cubature Kalman filter is derived. The suboptimal fading factor is calculated in a new method, which reduces the number of calculations for the cubature points from three times to twice without calculating the Jacobian matrix. The simulation results indicate that the proposed filter has the ability to track the maneuvering target and achieve higher target tracking accuracy and thus verifies the effectiveness of the proposed filter.

Maneuvering Target Tracking Algorithm Based on IMMSCKF

2013 ◽  
Vol 419 ◽  
pp. 145-150
Author(s):  
Jian Wang Hu ◽  
Peng Zhou ◽  
Hao Xie ◽  
Le Luo ◽  
Hou Bo He
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Interacting Multiple Model ◽  
Weighted Sum ◽  
Square Root ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Switching Speed ◽  
Maneuvering Target ◽  
Cubature Kalman Filter

Aiming at the tracking filters are liable to diverge and the tracking precision is low when tracking nonlinear maneuvering target, an Interacting Multiple Model Square-root Cubature Kalman Filter (IMMSCKF) is developed by introducing Square-root Cubature Kalman Filter (SCKF) into Interacting Multiple Model (IMM). This method uses SCKF for filtering each model, the weighted sum of the outputs of all parallel SCKF is taken as the output of IMMSCKF. Simulation shows that IMMSCKF has higher precision, quicker model switching speed, and smaller calculation cost compared with IMMUKF.

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