Target tracking methods based on a signal-to-noise ratio model

Dai LIU; Yong-bo ZHAO; Zi-qiao YUAN; Jie-tao LI; Guo-ji CHEN

doi:10.1631/FITEE.1900679

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Target tracking methods based on a signal-to-noise ratio model

Regular Papers | Updated：2022-05-19

- Target tracking methods based on a signal-to-noise ratio model
- 基于信噪比模型的目标跟踪算法
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 12, Pages: 1804-1814(2020)
- Affiliations：
  
  National Lab of Radar Signal Processing, Xidian University, Xi'an 710071, China
  Xi'an Electronic Engineering Research Institute, Xi'an 710100, China
- Author bio：
  
  Yong-bo ZHAO, E-mail: ybzhao@xidian.edu.cn
- Funds：
- DOI：10.1631/FITEE.1900679
  CLC： TN953
- Received：04 December 2019，
  
  Revised：2020-11-13，
  
  Published：2020-12
- Accepted：
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Dai LIU, Yong-bo ZHAO, Zi-qiao YUAN, et al. Target tracking methods based on a signal-to-noise ratio model[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1804-1814.
DOI：

Dai LIU, Yong-bo ZHAO, Zi-qiao YUAN, et al. Target tracking methods based on a signal-to-noise ratio model[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1804-1814. DOI： 10.1631/FITEE.1900679.

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。

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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references

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