基于动态时间规整距离指纹匹配的Wi-Fi网络室内定位算法

Wi-Fi网络中常规的基于指纹匹配室内定位算法面临信号时变现象或人为干扰的影响，导致定位精度不高。为此，提出基于动态时间规整（DTW）距离相似性指纹匹配的Wi-Fi网络室内定位算法。首先，该算法将定位区域的Wi-Fi信号特征按照采样的先后顺序转化为时间序列类型指纹，通过计算Wi-Fi信号指纹动态时间规整距离的大小来获取定位点与样本点的相似性；然后，根据采样区域结构特征，将Wi-Fi信号指纹采集问题划分为三类基本的动态路径采样方式；最后，结合多种动态路径采样方式增加指纹特征信息的准确性和完整性，从而提高指纹匹配的准确性和定位精度。大量实验结果表明，较瞬时指纹匹配定位算法，所提算法误差范围在3m以内定位的累积错误率：路径区域匀速运动提高了10%，变速运动提高了13%；开放区域交叉曲线运动提高了9%，S型曲线运动提高了3%。所提算法在实际室内定位应用中能有效提高指纹匹配的准确性和定位精度。

Fingerprint matching indoor localization algorithm based on dynamic time warping distance for Wi-Fi network

Focusing on the low accuracy problem of regular fingerprint matching indoor localization algorithm for Wi-Fi network confronted with signal fluctuation or jamming, the fingerprint matching indoor localization algorithm based on Dynamic Time Warping (DTW) similarity for Wi-Fi network was proposed. Firstly, the Wi-Fi signal characteristics in localization area were converted to the time-series fingerprints according to the sequence of sampling. The similarity between the locating data and sampling data was obtained by computing the fingerprint DTW distance of Wi-Fi signal. Then, according to the structural characteristics of the sampling area, the fingerprint sampling problem of Wi-Fi signal was divided into three kinds of basic sampling methods based on dynamic path. Finally, the accuracy and completeness of the fingerprint feature information were increased by the combination of multiple dynamic path sampling methods, which improved the accuracy and location precision of fingerprint matching. The extensive experimental results show that, compared with the instantaneous fingerprint matching indoor localization algorithm, within the location error of 3 m, the cumulative error frequency of the proposed localization algorithm, was 10% higher for uniform motion and 13% higher for variable motion within routing area, and 9% higher for crossed curvilinear motion and 3% higher for S-type curvilinear motion within open area. The proposed localization algorithm can improve accuracy and location precision of fingerprint matching effectively in real indoor localization applications.