WiFi fingerprint positioning is a common method for indoor location determination. Existing methods are susceptible to fluctuations in WiFi signal strength during the offline phase, leading to unevenly received signals. Additionally, during online positioning, there is a lack of integration with historical trajectory information. These problems can result in errors in both offline fingerprint acquisition and online location positioning. To address these problems, we propose a method that combines normality detection in the offline phase and Location Weighted K-nearest Neighbor positioning in the online phase. In the offline phase, initial Received Signal Strength Indication samples undergo preprocessing based on skewness and kurtosis for normality detection. If the samples conform to a normal distribution model, the probability density is estimated using the normal distribution function. If not, estimation occurs using the kernel density function model. Subsequently, values are averaged after Kalman filtering to establish a high-precision fingerprint database. During the online positioning phase, the LWKNN algorithm is employed. Initially, the Weighted K-nearest Neighbor method estimates the position, and this information is utilized as features to construct a Longterm and Shortterm Memory network model. The optimal path is determined through the least square method. Finally, the obtained outputs are integrated with historical data from the fingerprint positioning trajectory to enhance target positioning accuracy. Experimental results demonstrate that our indoor localization method significantly improves WiFi fingerprint localization accuracy compared to traditional localization methods.

中文翻译：

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基于机器学习的WiFi信号不均匀指纹定位框架

WiFi指纹定位是室内定位的常用方法。现有方法在离线阶段容易受到WiFi信号强度波动的影响，导致接收信号不均匀。另外，在线定位时，缺乏与历史轨迹信息的整合。这些问题都会导致离线指纹采集和在线位置定位出现错误。为了解决这些问题，我们提出了一种将离线阶段的正态性检测和在线阶段的位置加权K近邻定位相结合的方法。在离线阶段，初始接收信号强度指示样本根据偏度和峰度进行预处理，以进行正态性检测。如果样本符合正态分布模型，则使用正态分布函数估计概率密度。如果不是，则使用核密度函数模型进行估计。随后，对卡尔曼滤波后的值进行平均，建立高精度的指纹数据库。在线定位阶段采用LWKNN算法。最初，加权 K 最近邻方法估计位置，并将该信息用作构建长期和短期记忆网络模型的特征。通过最小二乘法确定最优路径。最后，将获得的输出与指纹定位轨迹的历史数据相结合，以提高目标定位精度。实验结果表明，与传统定位方法相比，我们的室内定位方法显着提高了 WiFi 指纹定位精度。