360° Scanning of Stationary Objects inside Room With a 24 GHz Commercial RADAR Module

Abstract

In this work, various stationary objects were detected within a room measuring 4.88 × 3.91 m². The data were captured by a 24 GHz commercial radar module, OPS243-C. The experiments were performed in two scenarios: over each wall or at different angles of the radar positions, and, secondly, by mounting this radar module on a rotating motor to scan each object over a 360° coverage. In addition, to suppress background noise and filter out strong signals in the heatmap results, a noise threshold and a Gaussian smoothing filter were applied. The predicted distances of objects were then obtained by applying suitable curve-fitting models to radar data captured in each scenario. The corresponding images captured in each scenario were used to validate the 2D and 3D heatmaps for object distances after curve fitting. Thus, for each wall scan, a linear curve-fitting model was found to be sufficient, whereas for entire-room scanning, a 6th-order polynomial curve-fitting model was found to provide the best predicted distances by the module. The methodology discussed in this work is useful for analyzing field-grown crops and continuously monitoring their health.