You are here

Neuro-Cognitive Radar-Imagery Segmentation and Identification (Neuro-RSI)

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC20C0575
Agency Tracking Number: 206750
Amount: $124,988.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: S5
Solicitation Number: SBIR_20_P1
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-08-27
Award End Date (Contract End Date): 2021-03-01
Small Business Information
888 Easy Street
Simi Valley, CA 93065-1812
United States
DUNS: 611466855
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Francisco Maldonado
 (805) 582-0582
Business Contact
 Emily Melgarejo
Phone: (805) 582-0582
Research Institution

nbsp;To provide NASA with innovative machine learning segmentation, classification, and analysis technology for radar imagery, specifically Synthetic Aperture Radar, American GNC Corporation (AGNC) and The Pennsylvania State Universityrsquo;s Radar and Communication Laboratory (PSU-RCL) propose the Neuro-Cognitive Radar-Imagery Segmentation and Identification (Neuro-RSI) system. Looking to ensure an adaptable and flexible system, Artificial Intelligence techniques enable a cognitive approach that emulates the human analysis process of SAR imagery data. While there have been advances related to development of new technologies for SAR Systems as well as methods for the analysis of their imagery, a generalized SAR imagery software analysis toolbox does not exist. The Neuro-RSI aims to provide a system that can be applied to diverse SAR technologies by a system architecture with cutting-edge low-level processing and segmentation as well as state-of-the-art pattern recognition, self-learning, and high-level cognition. This new AI-based SAR imagery analysis system is intended to then be deployed for enhancing NASArsquo;s automatic image analysis needs such in the Earth Science Data Systems Program. Innovations include: (1) flexible and reconfigurable software architecture that dynamically tunes the processing flow according to analysis goals; (2) cognitive analysis building on incremental system knowledge, adaptive SAR imagery analysis, and logic inference mechanisms; (3) integral approach to optimize the processing framework to characteristics of SAR systems; (4) pattern recognition schemes based on a deep learning framework; and (5) goal oriented agent that enables expansion of analysis capability working with a Query Set.

* Information listed above is at the time of submission. *

US Flag An Official Website of the United States Government