Indoor Localization based on Short-Range Radar and Rotating Landmarks

A novel concept for indoor self-localization based on rotating artificial landmarks with known locations using short-range radar is proposed. First, a processing pipeline for extracting range and angle measurements to the landmarks from a raw radar image is introduced, which consists of a neural network for distance estimation and a basic angle-of-arrival estimator. Second, a particle filter for tracking the pose based on the range and angle measurements is developed. Due to the ability of radar to measure range rate, i.e., the velocity in the direction of a detection, it is possible to robustly detect and localize rotating landmarks with the help of their micro-Doppler pattern. In this way, localization is possible even under difficult conditions (e.g., light changes). Experiments with a wheeled mobile robot and common office fans as landmarks demonstrate the effectiveness of the approach for indoor localization.