计算机工程与应用Issue(22)：228-232,5.DOI:10.3778/j.issn.1002-8331.1411-0350
WSNs中基于隐马尔科夫模型的目标识别问题研究
Target classification based on hidden Markov model in Wireless Sensor Networks
摘要
Abstract
It is challenging to classify multiple targets in wireless sensor networks based on the time-varying and continuous signals. In this paper, Hidden Markov Model is utilized as a framework for classification. The states in the HMM represent various combinations of vehicles of different types. With a sequence of observations, Viterbi algorithm is used at each sensor node to estimate the most likely sequence of states. Simulation results show that it reduce transmission more than 10%while maintaining identification rate.关键词
目标识别/无线传感器网络/隐马尔可夫模型/维特比算法Key words
target classification/wireless sensor networks/hidden Markov models/Viterbi分类
信息技术与安全科学引用本文复制引用
杨明霞,王万良,邵鹏飞..WSNs中基于隐马尔科夫模型的目标识别问题研究[J].计算机工程与应用,2015,(22):228-232,5.基金项目
国家自然科学基金面上项目（No.61379123）；浙江省自然科学基金项目（No.LQ12F03011，No.LQ14F020005，No.LY13F030011）；宁波市自然科学基金（No.2012A610016）；2013浙江省重点实验室开放基金项目（No.2013026）；衢州学院师资队伍建设基金（No.XNZQN201308）。 （）
