A fingerprint location framework for uneven WiFi signals based on machine learning

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.