ARTI: An Adaptive Radio Tomographic Imaging System

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Utah

Abstract

Radio tomographic imaging systems use received signal strength measurements between static wireless sensors to image the changes in the radio propagation environment in the area of the sensors, which can be used to localize a person causing the change. To date, spatial models used for such systems are set a priori and do not change. Imaging and tracking performance suffers because of the mismatch between the model and the measurements. Collecting labeled training data requires intensive effort, and the data degrade quickly as the environment changes. This paper provides a means for a radio tomographic imaging system to bootstrap to improve its spatial models using unlabeled data, iteratively improving itself over time. A collection of tracking filters are presented to improve the accuracy of image and coordinate estimates. This paper presents an online method to use these estimates to instantaneously update spatial model parameters. Further, a smoothing method is presented to fine-tune the model with a given finite latency. The development efforts are evaluated using simulations and validated with real-world experiments conducted in three different environments. With respect to another state-of-the-art radio tomographic imaging system, the results suggest that the presented system increases the median tracking accuracy by twofold in the most challenging environment and by threefold when the model parameters are trained using the smoothing method.

Details

Original languageEnglish
Article number7843639
Pages (from-to)7302-7316
Number of pages15
JournalIEEE Transactions on Vehicular Technology
Volume66
Issue number8
Publication statusPublished - 1 Aug 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • Bayesian filtering and smoothing, device-free localization, received signal strength, RF sensor networks, RF tomography

ID: 15324078