scholarly journals Levenberg-Marquardt Method Based Iterative Square Root Cubature Kalman Filter and its Applications to Maneuvering Re-entry Target Tracking

2018 ◽  
Author(s):  
Mu Jing ◽  
Wang Changyuan
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Square Root ◽  
Marquardt Method ◽  
Cubature Kalman Filter ◽  
Levenberg Marquardt

A consensus-based square-root cubature Kalman filter for manoeuvring target tracking in sensor networks

2020 ◽  
Vol 42 (15) ◽  
pp. 3052-3062
Author(s):  
Zenan Zhong ◽  
Enjiao Zhao ◽  
Xin Zheng ◽  
Xinhua Zhao
Keyword(s):  
Kalman Filter ◽  
Sensor Networks ◽  
Target Tracking ◽  
Estimation Accuracy ◽  
Square Root ◽  
Cubature Kalman Filter ◽  
Comparison Results ◽  
Adaptive Adjustment ◽  
Tracking Model ◽  
Global Accuracy

In this paper, a novel distributed tracking method is proposed for the problem of manoeuvring target tracking in sensor networks. Firstly, an adaptive adjustment tracking model is established by extended state observer (ESO) theory. Then, the consensus-based square-root cubature Kalman filter (SCKF) algorithm is proposed in order to improve the global accuracy and stability. In addition, the integrated model could reduce the influence of measurement noise. Finally, simulation is performed to verify the effectiveness of the scheme, whereby comparison results show that the estimation accuracy of the method proposed is higher than that of the traditional ESO and SCKF.

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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