RCS feature-aided insect target tracking algorithm

被引：0
|
作者：
Fang L. [1 ,2 ]
Zhou C. [1 ,2 ]
Wang R. [1 ,2 ]
Hu C. [1 ,2 ]
机构：
[1] Radar Research Lab, School of Information and Electronics, Institute of Technology, Beijing
[2] Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute of Technology), Ministry of Education, Beijing
来源：
Journal of Radars | 2019年 / 8卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Entomological radar; Feature aided; Radar Cross Section fluctuating; Target tracking;
D O I：
10.12000/JR19067
中图分类号：
学科分类号：
摘要：
Pest migration has the characteristics of large scale and strong suddenness, which will lead to the outbreaks of pests and diseases, the decline of grain yield, and considerable economic losses. Entomological radar is an effective means of monitoring migratory pests. However, the Radar Cross Section (RCS) of an insect target is small, whereas the echo power is weak. High detection probability will result in a high false alarm probability. In the data association step of target tracking, the association error occurs due to the influence of false measurement. By utilizing the amplitude difference between the target and noise, the amplitude information-assisted tracking algorithm can effectively improve the recognition degree toward the target and noise and improve the tracking performance. However, the RCS fluctuation model of the target is needed as prior information to calculate the amplitude likelihood ratio. Therefore, in this paper, the insect RCS fluctuating characteristics are analyzed based on Ku-band entomological radar experiment data. The results show that gamma distribution can fit well the RCS probability distribution of the insect target. On this basis,we derive the amplitude likelihood ratio of the gamma fluctuation target in Gaussian white-noise background. By analyzing the simulation results and performance under different signal-to-noise ratios, measurement noises, and fluctuation model parameters, compared with probabilistic data association filter, the RCS feature-aided tracking algorithm can effectively improve the insect target tracking accuracy © 2019 Institute of Electronics Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：598 / 605
页数：7
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