Multiple Target Tracking with RF Sensor Networks

TL;DR

This paper presents a real-time multiple target tracking system using RF sensor networks and radio tomographic imaging, capable of accurately tracking up to four targets indoors with low error, without requiring targets to carry devices.

Contribution

It introduces a novel RTI-based method combined with machine vision techniques for real-time multi-target tracking in cluttered indoor environments.

Findings

Accurately tracks up to 4 targets in real-time

Maximum average tracking error is below 0.55 meters

Effective in cluttered and intersecting trajectories environments

Abstract

RF sensor networks are wireless networks that can localize and track people (or targets) without needing them to carry or wear any electronic device. They use the change in the received signal strength (RSS) of the links due to the movements of people to infer their locations. In this paper, we consider real-time multiple target tracking with RF sensor networks. We perform radio tomographic imaging (RTI), which generates images of the change in the propagation field, as if they were frames of a video. Our RTI method uses RSS measurements on multiple frequency channels on each link, combining them with a fade level-based weighted average. We describe methods to adapt machine vision methods to the peculiarities of RTI to enable real time multiple target tracking. Several tests are performed in an open environment, a one-bedroom apartment, and a cluttered office environment. The results demonstrate that the system is capable of accurately tracking in real-time up to 4 targets in cluttered indoor environments, even when their trajectories intersect multiple times, without mis-estimating the number of targets found in the monitored area. The highest average tracking error measured in the tests is 0.45 m with two targets, 0.46 m with three targets, and 0.55 m with four targets.