论文标题
用于室内定位增强的半顺序概率模型
Semi-Sequential Probabilistic Model For Indoor Localization Enhancement
论文作者
论文摘要
本文提出了一个半序列的概率模型（SSP），该模型应用了额外的短期内存来增强概率室内定位的性能。常规的概率方法通常会不加区别地对待数据库中的位置。相反，由于用户在室内环境中的用户速度受到界限，因此SSP利用了先前位置的信息来确定可能的位置，并且上一个附近的位置的概率高于其他位置。尽管SSP利用了先前的位置信息，但它不需要用户的确切移动速度和方向。使用接收的信号强度指标（RSSI）和通道状态信息（CSI）指纹进行定位的现场实验表明，SSP可减少最大误差，并使现有概率方法的性能降低25％-30％。
This paper proposes a semi-sequential probabilistic model (SSP) that applies an additional short term memory to enhance the performance of the probabilistic indoor localization. The conventional probabilistic methods normally treat the locations in the database indiscriminately. In contrast, SSP leverages the information of the previous position to determine the probable location since the user's speed in an indoor environment is bounded and locations near the previous one have higher probability than the other locations. Although the SSP utilizes the previous location information, it does not require the exact moving speed and direction of the user. On-site experiments using the received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for localization demonstrate that SSP reduces the maximum error and boosts the performance of existing probabilistic approaches by 25% - 30%.
