Residual-Normalized Strong Tracking Filter for Tracking a Noncooperative Maneuvering Spacecraft

2019 ◽  
Vol 42 (10) ◽  
pp. 2304-2309
Author(s):  
Yuzi Jiang ◽  
Pengbin Ma ◽  
Hexi Baoyin
Keyword(s):  
Strong Tracking Filter ◽  
Tracking Filter ◽  
Maneuvering Spacecraft

Sensor Fusion Based on Strong Tracking Filter for Augmented Reality Registration

2011 ◽  
Vol 467-469 ◽  
pp. 108-113
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Augmented Reality ◽  
Inertial Sensor ◽  
Measurement Data ◽  
Sensor Data ◽  
Data Synchronization ◽  
Error Covariance ◽  
Tightly Coupled ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter

Accurate tracking for Augmented Reality applications is a challenging task. Multi-sensors hybrid tracking generally provide more stable than the effect of the single visual tracking. This paper presents a new tightly-coupled hybrid tracking approach combining vision-based systems with inertial sensor. Based on multi-frequency sampling theory in the measurement data synchronization, a strong tracking filter (STF) is used to smooth sensor data and estimate position and orientation. Through adding time-varying fading factor to adaptively adjust the prediction error covariance of filter, this method improves the performance of tracking for fast moving targets. Experimental results show the efficiency and robustness of this proposed approach.

scholarly journals High‐Order Extended Strong Tracking Filter

2021 ◽  
Vol 30 (6) ◽  
pp. 1152-1158
Author(s):  
SUN Xiaohui ◽  
WEN Tao ◽  
WEN Chenglin ◽  
CHENG Xingshuo ◽  
WU Yunkai
Keyword(s):  
High Order ◽  
Strong Tracking Filter ◽  
Tracking Filter

A novel method based on least squares support vector regression combing with strong tracking particle filter for machinery condition prognosis

2013 ◽  
Vol 228 (6) ◽  
pp. 1048-1062 ◽  
Author(s):  
Chengliang Li ◽  
Zhongsheng Wang ◽  
Shuhui Bu ◽  
Hongkai Jiang ◽  
Zhenbao Liu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Extend Kalman Filter ◽  
Combining Data ◽  
Novel Method ◽  
Strong Tracking Filter ◽  
Tracking Filter

A reliable prediction method is very important to avoid a catastrophic failure. This paper presents a novel method for machinery condition prognosis, named least squares support vector regression strong tracking particle filter which is based on least squares support vector regression combing with strong tracking particle filter. There are two main contributions in our work: first, the regression function of least squares support vector regression is extended, which constructs a bridge for the application of combining data-driven method with a recursive filter based on extend Kalman filter; second, an extend Kalman filter-based particle filter is studied by introducing a strong tracking filter into a particle filter. The strong tracking filter is used to update particles and produce importance densities which can improve the performance of the particle filter in tracking saltatory states, and finally strong tracking particle filter improves the prediction performance of least squares support vector regression in predicting saltatory states. In the experiment, it can be concluded that the proposed method is better than classical condition predictors in machinery condition prognosis.

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