Wi-Fi Indoor Location Method based on Improved Fingerprint Technique

One of the most popular indoor location techniques is fingerprint-based Wi-Fi location; however, the Wi-Fi location fingerprint database has issues including sparse samples and wireless access point (AP) distribution that greatly impact the location effect indoors. Because of this, we suggested a Wi-Fi indoor location technique that uses an improved fingerprint technique. First, to filter out more consistent and higher-quality APs during the fingerprint database construction phase, we employed an AP preference selection technique with combined reception percentage and variance. We then enlarged the fingerprint database in the appropriate area to increase the quality of the fingerprint database and lower the workload associated with its early establishment. In order to create a weighted K-Nearest Neighbors (WKNN) location technique, we finally fused the Pearson correlation coefficients at the matching location step. The WKNN location method using Pearson correlation coefficients and the AP-preferred fingerprint database improved location accuracy by 29.8%, according to test findings of real-world location scenarios. The technique can successfully reduce the cost of acquiring fingerprint databases while increasing location accuracy.