  Automotive targets undergoing turns in road junctions offer large synthetic
apertures over short dwell times to automotive radars that can be exploited for
obtaining fine cross-range resolution. Likewise, the wide bandwidths of the
automotive radar signal yield high-range resolution profiles. Together, they
are exploited for generating inverse synthetic aperture radar (ISAR) images
that offer rich information regarding the target vehicle's size, shape, and
trajectory which is useful for object recognition and classification. However,
a key requirement for ISAR is translation motion compensation and estimation of
the turning velocity of the target. State-of-the-art algorithms for motion
compensation trade-off between computational complexity and accuracy. An
alternative low complexity method is to use an additional sensor for tracking
the target motion. In this work, we propose to exploit computer vision
algorithms to identify the radar target object in the sensor field-of-view
(FoV) with high accuracy. Further, we propose to track the target vehicle's
motion through fusion of vision and radar data. Vision data facilitates the
accurate estimation of the lateral position of the target which complements the
radar capability of accurate estimation of range and radial velocity. Through
simulations and experimental evaluations with a monocular camera and Texas
Instrument millimeter wave radar we demonstrate the effectiveness of sensor
fusion for accurate target tracking for translational motion compensation and
the generation of high-quality ISAR images.
