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中文摘要：
针对基于Wi-Fi瞬时指纹定位算法中由于RSS信号的时变特性引起的Wi-Fi定位精度差问题,提出了一种基于滑动窗口最长公共子序列指纹定位算法.该算法将时间序列的RSS信号指纹转化为基于滑动窗口的数据模型,增加了指纹特征信息,提高比对准确性.通过计算请求定位数据与样本的最长公共子序列来获得样本点的相似性,解决由于窗口伸缩或滑动窗口中个别采样点无信号引起的比对不准确问题,从而提高了定位的精确性和鲁棒性.实验结果表明,所提定位算法的结果明显优于瞬时指纹定位算法.
英文摘要：
To reduce the negative effect in the Wi-Fi fingerprint localization algorithm caused by the fluctuation of the received signal strength （RSS）, a Wi-Fi fingerprint localization algorithm based on sliding window combined with the longest common subsequence was proposed. First, the time sequence RSS fingerprints were converted to the sliding window data model to increase the fingerprint characteristic information and improve the matching accuracy. And then, the requesting location data and the longest common subsequence were calculated to get the similarity of sampling points, which could solve the problem caused by the window scaling or the individual sampling point without signal in the sliding window, thereby the localization accuracy and robustness were improved. The results showed that the proposed localization algorithm was superior to the instantaneous fingerprints localization algorithm.
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