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CLC number: TN953
On-line Access: 2020-12-10
Received: 2019-12-04
Revision Accepted: 2020-02-09
Crosschecked: 2020-11-13
Cited: 0
Clicked: 5155
Citations: Bibtex RefMan EndNote GB/T7714
Target tracking methods based on a signal-to-noise ratio model
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Dai Liu, Yong-bo Zhao, Zi-qiao Yuan, Jie-tao Li, Guo-ji Chen. Target tracking methods based on a signal-to-noise ratio model[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1804-1814.
@article{title="Target tracking methods based on a signal-to-noise ratio model",
author="Dai Liu, Yong-bo Zhao, Zi-qiao Yuan, Jie-tao Li, Guo-ji Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="12",
pages="1804-1814",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900679"
}
%0 Journal Article
%T Target tracking methods based on a signal-to-noise ratio model
%A Dai Liu
%A Yong-bo Zhao
%A Zi-qiao Yuan
%A Jie-tao Li
%A Guo-ji Chen
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 12
%P 1804-1814
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900679
TY - JOUR
T1 - Target tracking methods based on a signal-to-noise ratio model
A1 - Dai Liu
A1 - Yong-bo Zhao
A1 - Zi-qiao Yuan
A1 - Jie-tao Li
A1 - Guo-ji Chen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 12
SP - 1804
EP - 1814
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900679
Abstract: In traditional target tracking methods, the angle error and range error are often measured by the empirical value, while observation noise is a constant. In this paper, the angle error 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 nonlinear filter method, the extended Kalman filter method based on the SNR model (SNR-EKF) and the unscented Kalman filter method based on the 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 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 the tracking performance compared with traditional tracking methods. The target tracking accuracy and convergence speed of the proposed methods have significant improvements.
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