Characterizing range progression of SAR point scatterers with recursive RANSAC

Abstract

GPS-denied navigation is an active area of research due to poor GPS coverage in urban canyons and possible jamming or spoofing from other sources. By utilizing exteroceptive sensors, such as cameras and more recently radar, landmarks within the environment can be identified and tracked to provide velocity and rate estimates to reduce the effects from integrated errors from IMU sensor drift. While techniques exist to identify landmarks in a single sensor scan, tracking these landmarks from frame to frame in the presence of measurement noise and spurious detections results in an NP-hard data association problem. With regards to synthetic aperture radar, previous methods employed to solve this data association are the Hough transform and the nearest neighbor algorithms. Unfortunately, the Hough transform is computationally expensive, and the nearest neighbor algorithm can diverge in high clutter. In this work, we apply the recursive-RANSAC multiple target tracking algorithm and efficiently track multiple unknown point scatterers in high clutter using sequential measurements of both synthetic and real radar returns.