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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.1900679


Target tracking methods based on SNR model


Author(s):  Dai Liu, Yong-bo Zhao, Zi-qiao Yuan, Jie-tao Li, Guo-ji Chen

Affiliation(s):  National Lab of Radar Signal Processing, Xidian University, Xi’ more

Corresponding email(s):   ybzhao@xidian.edu.cn

Key Words:  SNR model, Target tracking, The angle error, The range error, Non-linear filter


Dai Liu, Yong-bo Zhao, Zi-qiao Yuan, Jie-tao Li, Guo-ji Chen. Target tracking methods based on SNR model[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Dai Liu, Yong-bo Zhao, Zi-qiao Yuan, Jie-tao Li, Guo-ji Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900679"
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%J Journal of Zhejiang University SCIENCE C
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J0 - Journal of Zhejiang University Science C
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900679


Abstract: 
In traditional target tracking methods, angle and range errors are often measured by empirical value, while observation noise is a fixed constant. Angle and range error are analyzed. They are influenced by the signal to noise ratio (SNR). Therefore, a model related to SNR has been established, in which the SNR information is applied for target tracking. Combined with an advanced non-linear filter method, the extended Kalman filter method based on the SNR model (SNR-EKF) and the Unscented Kalman filter method based on SNR model (SNR-UKF) are proposed. There is little difference between the SNR-EKF and SNR-UKF methods in position precision, but the SNR-EKF method has advantages over in computation time, and the SNR-UKF method has advantages in velocity precision. Simulation results show that target tracking methods based on the SNR model can greatly improve tracking performance compared with traditional tracking methods. The target tracking accuracy and the convergence speed of the proposed methods represent significant improvements.

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