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Deep Learning Stationary Target Detector and Classifier

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911W6-19-C-0046
Agency Tracking Number: A2-7609
Amount: $537,497.19
Phase: Phase II
Program: SBIR
Solicitation Topic Code: A17-133
Solicitation Number: 17.2
Timeline
Solicitation Year: 2017
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-02-04
Award End Date (Contract End Date): 2020-04-30
Small Business Information
1845 West 205th Street
Torrance, CA 90501
United States
DUNS: 153865951
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Shahzad Khalid
 Director, Adaptive Computing
 (310) 320-3088
 ISProposals@poc.com
Business Contact
 Keith Baker
Phone: (424) 835-9475
Email: contracts@poc.com
Research Institution
N/A
Abstract

To address the Army’s need for an algorithm for enhanced detection and classification of stationary ground targets for fire control radar of Apache attack helicopters, Physical Optics Corporation (POC) proposes to mature, in Phase II, software for the new Deep Learning Stationary Target Detector and Classifier (DEESTAC) technology based on innovations in deep learning neural networks for enhancing radar resolution and classifying objects. During Phase I, POC successfully trained and tested neural networks with multiple radar datasets. We showed improved performance by using polarization data, evaluated effects of azimuth on classification accuracy, and demonstrated DEESTAC’s potential to detect and classify stationary ground targets as a function of signal-to-noise ratio. During Phase II, POC plans to further mature this technology by bringing the DEESTAC prototype to technology readiness level-6 with comprehensive testing. We will quantify computing power required to process the data in real time and deliver a low-size, weight, and power working prototype with an electronics test bed to facilitate independent testing by the Army. POC believes that the proposed DEESTAC technology, when fully developed, will be the most advanced and practical airborne stationary target detector and classifier, ideal for use in both the military and civilian commercial sectors.

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

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