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

• 作者: CHEN Sheng ZHAO Yongbo PANG Xiaojiao HU Yili CAO Chenghu
• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: ELECTRONICS TECHNOLOGY
• 页码范围: 538-544
• 页数: 7页
• 语种: 英文
• DOI: 10.23919/JSEE.2021.000045

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 per-formance of tracking radars.A frequency-agile refined maxi-mum likelihood(RML)algorithm based on optimal fusion is pro-posed.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 ob-tained for fusing the angle estimation.Through theoretical ana-lysis and simulation,the frequency-agile RML algorithm based on optimal fusion can improve the accuracy of angle estimation effectively.