现代雷达2013，Vol.35Issue(3)：37-40,4.
基于隐马尔科夫模型的RCS识别方法研究
A Study on RCS Recognition Method of Radar Targets Based on Hidden Markov Model
摘要
Abstract
RCS time series is decided by target characteristic of electromagnetic scattering and attitude motion characteristics, it contains the abundant information including material, size and framework, of the radar target. RCS is an important measure source to recognize the radar target. Hidden Markov Model ( HMM) is a kind of probability model represented by parametric for describing statistical characteristics of random process, it is a non-stationary random process without memory. HMM has the very strong ability to describe the characterization of time-varying signals, and it can classify the time-varying signals with different characteristics as a dynamic pattern classifier. In this paper the variation patterns of RCS was characterized by HMM, and the radar targets were recognized based on the different types of their variation patterns of RCS. The efficiency of the presented algorithm was showed with experimental results.
关键词
雷达散射截面/隐马尔科夫模型/目标识别Key words
RCS/ hidden markov model/ target recognition分类
信息技术与安全科学引用本文复制引用
郭武,朱明明,杨红兵..基于隐马尔科夫模型的RCS识别方法研究[J].现代雷达,2013,35(3):37-40,4.
