摘要
针对室内Wi-Fi环境的信号缺失问题,提出一种基于隐状态排序的半异构无线定位方法。介绍隐马尔可夫模型、隐状态排序方法,设计包含离线训练阶段和在线定位阶段的定位方法。实验结果表明,该方法在1 m误差范围内准确率达96.3%,能解决半异构特征向量的信号缺失问题,提高实际应用能力。
Facing the signal missing problem in the indoor Wi-Fi environment, this paper proposes a semi-heterogeneous wireless location method based on hidden state sorting. It introduces Hidden Markov Model（HMM） and hidden state sorting approach, designs the localization method including offiine training phase and online localization phase. Experimental results show that this method can achieve 96.3% accuracy within an error distance of 1 meter, greatly solve the signal missing problem of semi-heterogeneous feature vectors, and enhance its practical application capability.
出处
《计算机工程》
CAS
CSCD
2012年第17期280-283,共4页
Computer Engineering
基金
国家自然科学基金资助项目(61173066)
关键词
室内定位
信号强度
隐马尔可夫模型
隐状态
半异构特征
indoor location
signal intensity
Hidden Markov Model（HMM）
hidden state
semi-heterogeneous feature
