Low-angle estimation using frequency-agile refined maximum likelihood algorithm based on optimal fusion

被引：0
|
作者：
CHEN Sheng [1 ]
ZHAO Yongbo [1 ]
PANG Xiaojiao [1 ]
HU Yili [1 ]
CAO Chenghu [1 ]
机构：
[1] National Laboratory of Radar Signal Processing, Xidian University
来源：
JournalofSystemsEngineeringandElectronics | 2021年 / 32卷 / 03期
关键词：
frequency-agile; maximum likelihood; multipath signal; low-angle estimation;
D O I：
暂无
中图分类号：
TN957.51 [雷达信号检测处理];
学科分类号：
摘要：
Low elevation estimation, which has attracted wide attention due to the presence of specular multipath, is essential for tracking radars. Frequency agility not only has the advantage of strong anti-interference ability, but also can enhance the performance of tracking radars. A frequency-agile refined maximum likelihood(RML) algorithm based on optimal fusion is proposed. The algorithm constructs an optimization problem, which minimizes the mean square error(MSE) of angle estimation.Thereby, the optimal weight at different frequency points is obtained for fusing the angle estimation. Through theoretical analysis and simulation, the frequency-agile RML algorithm based on optimal fusion can improve the accuracy of angle estimation effectively.
引用
收藏
页码：538 / 544
页数：7
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