MA-RTI: Design and Evaluation of a Real-World Multipath-Assisted Device-Free Localization System

Abstract

Device-free localization (DFL) systems exploit changes in the radio frequency channel by measuring, for example, the channel impulse response (CIR), to detect and localize obstacles within a target area. However, due to a lack of well-defined interfaces, missing modularization, as well as complex system configuration, it is difficult to deploy DFL systems outside of laboratory setups. This paper focused on the system view and the challenges that come with setting up a DFL system in an indoor environment. We propose MA-RTI, a modular DFL system that is easy to set up, and which utilizes a multipath-assisted (MA) radio-tomographic imaging (RTI) algorithm. To achieve a modular DFL system, we proposed and implemented an architectural model for DFL systems. For minimizing the configuration overhead, we applied a 3D spatial model, that helps in placing the sensors and calculating the required calibration parameters. Therefore, we configured the system solely with idle measurements and a 3D spatial model. We deployed such a DFL system and evaluated it in a real-world office environment with four sensor nodes. The radio technology was ultra-wideband (UWB) and the corresponding signal measurements were CIRs. The DFL system operated with CIRs that provided a sub-nanosecond time-domain resolution. After pre-processing, the update rate was approximately 46 Hz and it provided a localization accuracy of 1.0 m in 50 % of all cases and 1.8 m in 80 % of all cases. MA fingerprinting approaches lead to higher localization accuracy, but require a labor-intensive training phase.

SALOS—A UWB Single-Anchor Indoor Localization System Based on a Statistical Multipath Propagation Model

“…UWB communication systems meet these requirements. While SALOS works with active tag UWB nodes, the multipath-assisted MA-RTI system from [ 17 ] follows a passive approach. The system tracks objects with a localization accuracy of 1–2 m by incorporating multipath propagation, without nodes on the objects for localization.…”

Marine Object Detection using YOLOv4 Adapted Convolutional Neural Network

“…With the advent of improved models like YOLOv7 and novel architectures incorporating attention mechanisms, the underwater object detection landscape is evolving rapidly [13]. Multisensor platforms and collaborative weakly supervised approaches have brought additional dimensions to the field, showcasing the interdisciplinary efforts required for robust underwater object detection [14].…”

Multipath-RTI: Millimeter-Wave Radio Based Device-Free Localization

“…However, only a few studies have been conducted on multipath-propagation-based RTI [8]- [11]. In [8], the feasibility study of applying static reflection multipath to devicefree localization is presented.…”

“…In [9], low-rank and sparse decomposition were used to remove image artifacts caused by multipath and improved the accuracy of RTI images. To improve the DFL accuracy by utilizing multipath propagation, Cimdins et al proposed multipath-assisted-RTI, which extracts the multipath components (MPCs) from the channel impulse response (CIR) of ultra-wideband signals, estimates a single bounce reflection path, and incorporates it into an RTI system [10], [11]. Although this method enables fast localization with a small number of anchor nodes, in some cases, the localization accuracy significantly deteriorates (for instance, when the target is located close to the anchor node), and a position accuracy of less than 1.0 m is obtained only 50 % of the time.…”