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

2020年第21卷第12期

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《信息与电子工程前沿（英文）》 >> 2020年第21卷第12期 doi: 10.1631/FITEE.1900679

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

Affiliation(s): National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China; Xi’an Electronic Engineering Research Institute, Xi’an 710100, China; less

收稿日期： 2019-12-04 录用日期： 2020-12-10 发布日期： 2020-12-10

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摘要

In traditional methods, the and are often measured by the empirical value, while observation noise is a constant. In this paper, the and 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 . Combined with an advanced 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 methods based on the SNR model can greatly improve the tracking performance compared with traditional tracking methods. The accuracy and convergence speed of the proposed methods have significant improvements.