Mitigation of RSSI Variations Using Frequency Analysis and Kalman Filtering

Abstract

Several indoor locating systems rely on the groundbreaking use of the received signal strength indication (RSSI). RSSI is a cost-effective and straightforward solution that does not necessitate the purchase of any additional hardware. Variations in RSSI of Bluetooth low energy (BLE) devices in indoor positioning degrade the precision of indoor positioning systems. This paper provides a filtering strategy for processing the RSSI signal to solve the aforementioned difficulty. The RSSI of the BLE node is captured. The raw RSSI signals are then filtered using various filtering methods and their combinations. This work employs a variety of filters including frequency analysis, Kalman filtering, frequency Kalman filtering and Kalman frequency filtering. The standard deviation value after applying various filters was compared under various test cases. The results suggest that applying frequency filters followed by Kalman filtering can reduce RSSI variability in BLE devices, resulting in more consistent and reliable RSSI measurements.