摘要
雷达散射截面(RCS)时间序列由目标电磁散射特性和姿态运动特性共同决定,包含了雷达目标的材质、尺寸和结构等信息,是实现雷达目标识别的重要测量量。隐马尔科夫模型(HMM)是一种用参数表示的用于描述随机过程统计特性的概率模型,是一个无记忆的非平稳随机过程,具有很强的表征时变信号的能力,非常适合作为动态模式分类器,对具有不同变化特性的时变信号进行分类识别。文中利用HMM表征雷达目标RCS序列变化模式(规律),根据不同类别目标RCS序列变化模式的差异对雷达目标进行分类识别。实测数据验证结果表明,该算法具有较高的识别概率。
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 rec-ognized based on the different types of their variation patterns of RCS. The efficiency of the presented algorithm was showed with experimental results.
出处
《现代雷达》
CSCD
北大核心
2013年第3期37-40,共4页
Modern Radar
