Radio Tomography for Roadside Surveillance

TL;DR

This paper advances radio tomographic imaging (RTI) for roadside surveillance by introducing modeling improvements and algorithms that enhance image quality and target recognition accuracy using wireless sensor networks.

Contribution

It proposes a physically motivated weight matrix, noise mitigation techniques, and frame combination methods to improve RTI performance in roadside scenarios.

Findings

Improved imaging quality for human-in-the-loop recognition

Enhanced automatic target recognition accuracy

Demonstrated benefits on measured data set

Abstract

Radio tomographic imaging (RTI) has recently been proposed for tracking object location via radio waves without requiring the objects to transmit or receive radio signals. The position is extracted by inferring which voxels are obstructing a subset of radio links in a dense wireless sensor network. This paper proposes a variety of modeling and algorithmic improvements to RTI for the scenario of roadside surveillance. These include the use of a more physically motivated weight matrix, a method for mitigating negative (aphysical) data due to noisy observations, and a method for combining frames of a moving vehicle into a single image. The proposed approaches are used to show improvement in both imaging (useful for human-in-the-loop target recognition) and automatic target recognition in a measured data set.