Indoor Object Sensing Using Radio-Frequency Identification With Inverse Methods

Abstract

Indoor device-free object sensing can be utilized in many applications such as occupant-centered control of building utilities and assisted living. To satisfy the mathematical requirement of many observation points for arbitrary indoor layout and furnishing, radio-frequency identification (RFID) offers a low-cost solution with a plethora of maintenance-free passive tags. Both the received signal strength indicator and carrier phase from tag backscattering can be assembled to generate the voxel reflectivity distribution by the inverse method. We adopt the regularized truncated pseudo-inverse method, and devise the strategies for optimal selection of the critical parameters. Compared with conventional matched filtering, our method is more robust against random noises in the collected data. An experimental prototype was established to evaluate the system robustness and performance, and the dipole antennas were used to replace patch antennas to enhance the system signal-to-noise ratio (SNR). The regularized truncated pseudo-inverse method together with the improved system SNR has successfully shown higher locating accuracy and lower computational cost.