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

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...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2020-12, Vol.21 (12), p.1804-1814
Main Authors: Liu, Dai, Zhao, Yong-bo, Yuan, Zi-qiao, Li, Jie-tao, Chen, Guo-ji
Format: Article
Language:English
Subjects:
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Electrical Engineering
Electronics and Microelectronics
Empirical analysis
Error analysis
Extended Kalman filter
Instrumentation
Methods
Networks
Nonlinear filters
Range errors
Signal to noise ratio
Tracking
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.1900679