Radar Based Humans Localization with Compressed Sensing and Sparse Reconstruction

Abstract

Localization and detection is a vital task in emergency rescue operations. Devastating natural disasters can create environments that are inaccessible or dangerous for human rescuers. Contaminated areas or buildings in danger of collapsing can be searched by rescue robots which are equipped with diverse sensors such as optical and radar sensors. In scenarios where the line of sight is blocked, e.g., by a wall, a door or heavy smoke or dust, sensors like LiDAR or cameras are not able to provide sufficient information. The usage of radar in these kinds of situations can drastically improve situational awareness and hence the likelihood of rescue. In this paper, we present a method that is used for radar imaging behind obstacles by utilizing a signal model that includes the floor reflection propagation path in addition to the direct path of the radar signal. Additionally, compressed sensing methods are presented and applied to real world radar data that was recorded by a Stepped Frequency Continuous Wave (SFCW) radar mounted on a semi-autonomous robot. The results show an improved radar image that allows the clear identification of persons behind obstacles.