Radar Fusion for Multipath Mitigation in Indoor Environments

Abstract

One of the main challenges of radar-based localization applications in indoor environments is the presence of strong multipath. When the radar bandwidth is large enough, multipath components can be resolved in range but they result in unwanted ghost targets. We propose a novel multipath mitigation approach that exploits the fact that multipaths are highly dependent on the scene geometry. The multipath mitigation approach discards the ghost targets based on the fused information of multiple radars located at different positions in the scene. For such radar fusion, the output of the radar signal processing chain is translated into the world coordinate system that is common for all the radars. We propose a radar alignment approach to estimate the translation and rotation parameters from radar to world coordinate system and vice versa. Our multipath mitigation method is combined with an unscented Kalman filter to improve the localization accuracy. We demonstrate the effectiveness of our complete approach with a real experiment using two radars to detect and track a target in a room with severe multipath.