Channel Sensing Based Distance Estimation in Backscattering RF Tag Networks

A backscatter tag-to-tag network enables battery-less communication by harvesting energy and reflecting wireless signals between tags, making it ideal for energy-efficient IoT applications such as asset tracking, structural health monitoring, and environmental sensing. Accurate localization is crucial for these applications. While RSSI-based (Received Signal Strength Indicator) localization is the most common method for RF localization - estimating distance based on the received signal strength - it is often dependent on the position and power of the excitation source. We present a novel distance estimation method based on the estimation of the channel path loss and phase between tags, which is independent of the excitation source's position and power. The experimental results demonstrate millimeter-level accuracy in 67% of cases and 99% accuracy within 17 cm for tag-to-tag distances up to 2.4 meters at 915 MHz.