Systems and methods for recognizing objects in radar imagery

• US 9,978,013 B2
• Filed: 07/08/2015
• Issued: 05/22/2018
• Est. Priority Date: 07/16/2014
• Status: Active Grant

• Current Assignee: General Dynamics Mission Systems Inc. (General Dynamics Corporation)
• Original Assignee: Deep Learning Analytics, LLC (General Dynamics Corporation)
• Inventors: Kaufhold, John Patrick
• Primary Examiner(s): Brainard, Timothy A
• Application Number:
  US14/794,376
• Publication Number:
  US 20160019458A1
• Time in Patent Office: 1,049 Days
• Field of Search: 342 25 F
• CPC Class Codes: G01S 13/90 using synthetic aperture techniques , e.g. synthetic aperture radar [SAR] techniques
• CPC Class Codes: G01S 13/9027 Pattern recognition for feature extraction
• CPC Class Codes: G01S 13/9029 specially adapted for moving target detection within a single SAR image or within multiple SAR images taken at the same time
• CPC Class Codes: G01S 13/904 SAR modes
• CPC Class Codes: G01S 7/417 involving the use of neural networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/0463 Neocognitrons

Abstract

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.

Claims

1. A system for assembling a database of labeled radar image chips for object recognition, comprising:

a flying vehicle;

a synthetic aperture radar device onboard the flying vehicle,

the synthetic aperture radar device configured to receive a stream of radio wave signals reflected from a target scene; and

a processor onboard the flying vehicle,

the processor configured to process the stream of radio wave signals received by the synthetic aperture radar device into a plurality of two-dimensional radar image chips, each chip including a set of pixels comprising magnitude and phase information derived from the received radio wave signals,

the processor further configured to wrangle at least one of a set of semantic labels from each of the plurality of two-dimensional radar image chips, each semantic label corresponding to an object in the target scene, and

the processor further configured to generate a database of labeled radar image chips as a result of the wrangling.