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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: 2136

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Dai Liu

https://orcid.org/0000-0001-7794-6468

Yong-bo Zhao

https://orcid.org/0000-0002-6453-0786

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.12 P.1804-1814

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


Target tracking methods based on a signal-to-noise ratio 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’an 710071, China; more

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

Key Words:  Signal-to-noise ratio (SNR) model, Target tracking, Angle error, Range error, Nonlinear filter


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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.

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volume="21",
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pages="1804-1814",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900679"
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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.

基于信噪比模型的目标跟踪算法

刘代1,2,赵永波1,袁子乔2,李杰涛2,陈国际2
1西安电子科技大学雷达信号处理国家重点实验室,中国西安市,710071
2西安电子工程研究所,中国西安市,710100

摘要:传统目标跟踪算法中测角误差和测距误差取经验值,量测噪声为常数。本文分析测角误差和测距误差的影响因素,发现它们都与目标信噪比相关。于是建立雷达信噪比模型,将信噪比信息应用到目标跟踪算法。结合先进的非线性滤波算法,提出利用信噪比的扩展卡尔曼滤波(SNR-EKF)算法和利用姿态角的不敏卡尔曼滤波(SNR-UKF)算法。SNR-EKF和SNR-UKF相比位置精度差距不大,但在计算耗时上SNR-EKF算法较优,速度精度上SNR-UKF占优。仿真结果表明,利用信噪比的目标跟踪算法相比传统的EKF、UKF算法目标跟踪性能得到很大提高,体现在跟踪精度显著提高,收敛速度显著加快。

关键词:信噪比模型;目标跟踪;角度误差;距离误差;非线性滤波

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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