隐马尔可夫模型和神经网络方法在雷达目标检测中的应用

R. Lahouari, B. Abdelkader, M. Larbi
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引用次数: 1

摘要

正如大多数技术领域一样,雷达和声纳最近的演变是显而易见的,因为信息处理能力的发展非常迅速。为了满足用户日益增长的需求,这种演变导致赋予雷达和声纳几种功能模式。本文介绍了雷达目标域探测中两种经典的数据处理方法。第一种方法是基于隐马尔可夫模型“HMM”,第二种方法是基于神经元网络方法“ANN”,其灵感最初来自于人类的智力功能
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of hidden Markov model and neural network approach for radar target detection
The recent evolution of radar and sonar is obvious, as that of most of the technical domains, by the extremely fast development of the information processing capacities. To answer for increasing necessities of the users, this evolution led to endow the radar and the sonar of several modes of functioning. In this article, two classical methods of data processing are suggested in detection of radar target domain. The first technique is based on hidden Markov model "HMM", so for the second is based on the neuron network approach "ANN", which inspired originally from intellectual functioning of the human being
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