Enable Practical Long-Range Multi-Target Backscatter Sensing

Abstract

Backscatter sensing has emerged as a significant technology within the Internet of Things (IoT), prompting extensive research interest. This paper presents LoMu, the first long-range multi-target backscatter sensing system designed for low-cost tags operating under ambient LoRa. LoMuintroduces an orthogonal sensing model that processes backscatter signals from multiple tags to extract motion information. The design addresses several practical challenges, including near-far interference among multiple tags, phase offsets from unsynchronized transceivers, and phase errors due to frequency drift in low-cost tags. To overcome these issues, we propose a conjugate-based energy concentration method to extract high-quality signals and a Hamming-window-based method to mitigate the near-far problem. Additionally, we exploit the relationship between excitation and backscatter signals to synchronize the transmitter (TX) and receiver (RX) and combine double sidebands of backscatter signals to eliminate tag frequency drift. Furthermore, a novel joint estimation algorithm is introduced to exploit both amplitude and phase information in target signals, enhancing frequency sensing results and robustness. Our implementation and extensive experiments demonstrate that LoMucan accurately sense up to 35 tags simultaneously and achieve an average frequency sensing error of 0.5% at a range of 400 meters, which is $4\times$ the range of the state-of-the-art.