Extremely low-cost passive RFID tags have become key components for the Internet of Things (IoT), where ubiquitous object locating is one of the most important functions. Although the number of tags in indoor environment continues growing fast, objects cannot be always assumed tagged, whether intentionally or unintentionally. Compared to tagged objects, a tagless target which neither emits nor modulates signal is much more difficult to locate, especially when the size is small. In this paper, we achieve accurate tagless object locating through a dense and wide non-uniform sampling in the Fourier domain of target reflectivity with the help of ambient passive RFID tags as landmarks. Unlike conventional RFID systems, we leverage nonlinearity in passive tags to backscatter second harmonic signals. The frequency separation of uplink and downlink in harmonic RFID allows ready interference cancellation. We embrace spatial diversity enabled by ambient tags and frequency diversity by broadband harmonic backscattering to improve accuracy and robustness. We present the fundamental theory and a prototype system to verify the proposed approach.
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