LMS coefficient filtering for Time-varying chirped signals
LMS coefficient filtering for Time-varying chirped signals
复制标题
针对时变线性调频信号的 LMS 系数滤波
DOI：
10.1109/tsp.2004.836529
复制
发表时间：
2004
影响因子：
5.4
通讯作者：
Roger Francis Woods
中科院分区：
文献类型：
--
作者：
L. Ting;C. Cowan;Roger Francis Woods
本文提出了最小均方 (LMS) 算法中的系数滤波技术，以提高时变线性调频信号的自适应预测器跟踪性能。本文使用的示例应用是用于检测雷达啁啾脉冲的电子支持测量 (ESM) 接收器。研究了泄漏 LMS、动量 LMS 和提出的未来状态系数 (FC-LMS) 滤波算法。泄漏LMS算法能够消除啁啾信号初始收敛时变频率的记忆效应，从而提高雷达脉冲检测性能。动量LMS能够更有效地搜索时变最佳权重解，而FC-LMS使用并行技术来保留LMS吞吐量，同时与标准LMS算法相比能够对啁啾信号表现出更好的跟踪性能。
This paper presents coefficient filtering techniques in the least mean squares (LMS) algorithm to improve adaptive predictor tracking performance for time-varying chirped signals. The example application used in this paper is an electronic support measure (ESM) receiver for detecting radar chirped pulses. The leakage LMS, momentum LMS, and the proposed future-state coefficient (FC-LMS) filtering algorithms have been studied. The leakage LMS algorithm has the ability to remove the memory effect of the initial converged time-varying frequency of the chirped signal, thus improving the radar pulse detection performance. The momentum LMS is able to search for the time-varying optimum weight solution more efficiently, and the FC-LMS uses a parallel technique to retain the LMS throughput while being able to show a better tracking performance for chirped signals compared with the standard LMS algorithm.