Device-free human localization and tracking with UHF passive RFID tags: A data-driven approach

Abstract

Localizing and tracking human movement in a device-free and passive manner is promising in two aspects: i) it neither requires users to wear any sensors or devices, ii) nor it needs them to consciously cooperate during the localization. Such indoor localization technique underpins many real-world applications such as shopping navigation, intruder detection, surveillance care of seniors etc. However, current passive localization techniques either need expensive/sophisticated hardware such as ultra-wideband radar or infrared sensors, or have an issue of invasion of privacy such as camera-based techniques, or need regular maintenance such as the replacement of batteries. In this paper, we build a novel data-driven localization and tracking system upon a set of commercial ultrahigh frequency passive radio-frequency identification tags in an indoor environment. Specifically, we formulate human localization problem as finding a location with the maximum posterior probability given the observed received signal strength indicator from passive radio-frequency identification tags. In this regard, we design a series of localization schemes to capture the posterior probability by taking the advance of supervised-learning models including Gaussian Mixture Model, k Nearest Neighbor and Kernel-based Learning. For tracking a moving target, we mathematically model the task as searching a location sequence with the most likelihood, in which we first augment the probabilistic estimation learned in localization to construct the Emission Matrix and propose two human mobility models to approximate the Transmission Matrix in the Hidden Markov Model. The proposed tracking model is able to transfer the pattern learned in localization into tracking but also reduce the location-state candidates at each transmission iteration, which increases both the computation efficiency and tracking accuracy. The extensive experiments in two real-world scenarios reveal that our approach can achieve up to 94% localization accuracy and an average 0.64m tracking error, outperforming other state-of-the-art radio-frequency identification based indoor localization systems.

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

“…Ruan et al [ 23 ] developed a localization and tracking system using UHF passive RFID tags. In a residential evaluation in three rooms (approximately 45 m 2 of combined size), 34 tags were deployed and four antennas.…”

“…System Plils was developed by Li et al [ 24 ], it utilizes 2.4 GHz signals and was evaluated in an environment of area of 24 m 2 , while utilizing 10 reference nodes and one wireless reader to track position of a robot with accuracy of 0.75 m. They emphasize on using cheap off-the-shelf wireless chip, but nevertheless such density of devices is not possible in real-world deployments. Although the results in [ 23 , 24 ] present better average errors of localizations, these system are not usable in the IoT enabled homes. As shown, passive RFID-only approaches do not match the accuracy of MFAM if real-world-deployment constriction of number of the tags is considered.…”

Localization of Moving Objects Based on RFID Tag Array and Laser Ranging Information

“…Liang et al introduced a device-free indoor localization system based on particle swarm optimization which uses RSS and phase information measured by RFID readers to localize the target [11]. Ruan et al proposed a data driven approach for human localization and tracking [12] and previously, they also used RSS in another research [13] and achieved a reasonable level of accuracy in both approaches; however, RSS is easily affected by environmental noise and other interferences. RFID phase readings are also used for localization but these methods provide less accuracy due to phase ambiguity.…”

Research on RSS Data Optimization and DFL Localization for Non-Empty Environments

“…Wang et al proposed a saddle model [30] and improved ellipse model [31], and achieved localization through improved Bayesian filtering and particle filtering. Ruan et al presented and realized a data-driven approach which is based on the Gaussian Mixture Model (GMM)-Hidden Markov Model (HMM) and k Nearest Neighbor (KNN)-HMM model [32].…”