A BLE Indoor Positioning Algorithm based on Weighted Fingerprint Feature Matching Using AOA and RSSI

As a kind of indoor positioning technology, Bluetooth Low Energy (BLE) has the characteristics of low power consumption, low cost, high accuracy, etc. In Bluetooth Specification 5.1, the Angel of Arrival and Angel of Departure methods (AOA&AOD) method is introduced in order to get better positioning accuracy. However, in the actual environment, factors such as signal interference and antenna directivity will cause the angle information to be inaccurate and lead to large positioning errors. Therefore, this paper proposes a weighted fingerprint feature matching algorithm using AOA and RSSI (WFFMA) to improve positioning accuracy. In the fingerprint database establishment stage, Natural Breaks (NB) classification is introduced to extract features as fingerprint values. Then Random Forest (RF) is used to train the weight of each feature. In the online matching stage, improved K-nearest-neighbor (KNN) method is proposed to get the predicted coordinates. The proposed WFFMA is verified through experiments based on the hardware platform. The experimental results show that the accuracy of the proposed algorithm is improved significantly, compared with existing traditional algorithms.