DMLF: Device-Free Multitarget Adaptive Backscatter Link Fingerprint Localization

In the realm of smart homes, the emphasis lies in fostering seamless interaction between user-device and device-device within their environment, bringing convenience to both work and life. However, users may not always carry signal transceiving devices to establish connections with the environment. In such cases, device-free localization (DFL) plays a pivotal role. DFL involves identifying targets, precisely locating them, and providing feedback to other devices, thereby filling the information gap for targets without carried interactive devices. Most traditional passive UHF RFID localization methods can only achieve single-target localization at a time, while many application scenarios require multi-target localization. To address this challenge, this paper proposes a device-free multi-target Link Fingerprint (DMLF) localization method. DMLF analyzes the impact of targets on the UHF RFID links, extracts target-specific features from the observation data. A novel approach to link fingerprint localization is introduced, involving the compression and reconstruction of feature information into link fingerprints. Furthermore, considering the characteristics of link fingerprints, virtual reference fingerprints and dynamic fingerprint clustering are introduced to enhance localization accuracy and reduce computational overhead. There is no need for personnel to collect fingerprint data at sampling points during the offline phase, compared to received signal strength indicator (RSSI) fingerprints, link fingerprints provide a more accurate reflection of the target’s position and exhibit stronger robustness. This method reduces the storage requirements for the fingerprint database and computational complexity. Experimental results indicate that, in comparison to traditional localization methods, the DMLF algorithm achieves higher accuracy. The average localization accuracies for scenarios with 1 to 5 targets are 0.2679m, 0.3962m, 0.4937m, 0.7637m, and 1.2226m, respectively, with link fingerprints contributing to the enhanced precision of localization.