Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery

Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks

Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery

Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks

Path Loss Modeling of RFID Backscatter Channels With Reconfigurable Intelligent Surface: Experimental Validation

In the realm of radio frequency identification (RFID) technology, the integration of reconfigurable intelligent surfaces (RISs) has opened up new possibilities for real-time remote data capturing and seamless connectivity. By manipulating the electromagnetic properties of the environment, RIS enables the control of electromagnetic wave propagation and allows for virtual line-of-sight (LOS) in cases where physical LOS is blocked. This has tremendous implications for the future of RFID applications, particularly with the emergence of chipless RFID technology. In this regard, this paper develops free-space path loss models for RIS-assisted RFID wireless communications. The proposed models in this study have taken into account several crucial physical factors, including tag radar cross-section (RCS), the physical properties of the RIS, and the radiative near-field/far-field effects of the RIS. To further validate the theoretical findings, we have conducted experimental measurements using a fabricated RIS. Numerical simulations were also utilized to validate the models and verify our findings. The channel measurements have demonstrated good agreement with the proposed path loss models, further bolstering the potential of RIS-assisted RFID wireless communications