An open-source project for accurate indoor pedestrian localization using WiFi, PDR, and EKF fusion. It combines WiFi signals, step tracking, and sensor fusion for reliable positioning in GPS-denied environments.

This project introduces how to conduct indoor positioning using multi-sensors, including WiFi and mobile sensors, such as accelerometer and magnetometer.We use KNN algorithm to deal with WiFi RSS and use accelerometer and magnetometer to study stepping information of pedestrian.

Many real world applications need to know the localization of a user in the world to provide their services. Therefore, automatic user localization has been a hot research topic in the last years. Automatic user localization consists of estimating the position of the user (latitude, longitude and altitude) by using an electronic device, usually a mobile phone. Outdoor localization problem can be solved very accurately thanks to the inclusion of GPS sensors into the mobile devices. However, indoor localization is still an open problem mainly due to the loss of GPS signal in indoor environments. Although, there are some indoor positioning technologies and methodologies, this database is focused on WLAN fingerprint-based ones (also know as WiFi Fingerprinting).