The present invention is directed to systems and methods for detecting objects in a
radar image
stream. Embodiments of the invention can receive a
data stream from
radar sensors and use a deep neural network to convert the received
data stream into a set of semantic labels, where each semantic
label corresponds to an object in the
radar data stream that the deep neural network has identified.
Processing units running the deep neural network may be collocated onboard an airborne vehicle along with the radar sensor(s). The
processing units can be configured with powerful, high-speed
graphics processing units or field-programmable gate arrays that are low in size, weight, and power requirements. Embodiments of the invention are also directed to providing innovative advances to object recognition training systems that utilize a
detector and an object recognition
cascade to analyze radar image streams in real time. The object recognition
cascade can comprise at least one recognizer that receives a non-background
stream of image patches from a
detector and automatically assigns one or more semantic labels to each non-
background image patch. In some embodiments, a separate recognizer for the
background analysis of patches may also be incorporated. There may be multiple detectors and multiple recognizers, depending on the design of the
cascade. Embodiments of the invention also include novel methods to tailor deep neural network algorithms to successfully process radar imagery, utilizing techniques such as normalization, sampling, data augmentation, foveation, cascade architectures, and
label harmonization.