Tracking Filter Engineering: The Gauss-Newton and polynomial filters

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

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High-accuracy fault feature extraction for rolling bearings under time-varying speed conditions using an iterative envelope-tracking filter

Journal of Sound and Vibration ◽

10.1016/j.jsv.2019.02.026 ◽

2019 ◽

Vol 448 ◽

pp. 211-229 ◽

Cited By ~ 18

Author(s):

Shiqian Chen ◽

Minggang Du ◽

Zhike Peng ◽

Ming Liang ◽

Qingbo He ◽

...

Keyword(s):

Feature Extraction ◽

High Accuracy ◽

Time Varying ◽

Envelope Tracking ◽

Rolling Bearings ◽

Tracking Filter ◽

Fault Feature Extraction

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Analytical solution of /spl alpha/-/spl beta/-/spl Gamma/ colored noise tracking filter with a noisy jerk as correlated target maneuver model

Proceedings of the 1997 American Control Conference (Cat. No.97CH36041) ◽

10.1109/acc.1997.611926 ◽

1997 ◽

Author(s):

C.C. Arcasoy

Keyword(s):

Analytical Solution ◽

Colored Noise ◽

Tracking Filter

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Biased Estimation Properties of the Pseudolinear Tracking Filter

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/taes.1982.309250 ◽

1982 ◽

Vol AES-18 (4) ◽

pp. 432-441 ◽

Cited By ~ 116

Author(s):

Vincent Aidala ◽

Steven Nardone

Keyword(s):

Biased Estimation ◽

Tracking Filter

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Non-linear polynomial filters for edge enhancement of mammogram lesions

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2016.01.007 ◽

2016 ◽

Vol 129 ◽

pp. 125-134 ◽

Cited By ~ 22

Author(s):

Vikrant Bhateja ◽

Mukul Misra ◽

Shabana Urooj

Keyword(s):

Edge Enhancement ◽

Linear Polynomial ◽

Non Linear ◽

Polynomial Filters

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Sensor Fusion Based on Strong Tracking Filter for Augmented Reality Registration

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.108 ◽

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.

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