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INTUIDEX INC SBIR Phase II Award, November 2022

A SBIR Phase II contract was awarded to Intuidex, Inc. in November, 2022 for $1,249,989.0 USD from the U.S. Department of Defense and United States Air Force.

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Contents

sbir.gov/node/2568973
Is a
SBIR/STTR Awards
SBIR/STTR Awards
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SBIR/STTR Award attributes

SBIR/STTR Award Recipient
Intuidex, Inc.
Intuidex, Inc.
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Government Agency
U.S. Department of Defense
U.S. Department of Defense
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Government Branch
United States Air Force
United States Air Force
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Award Type
SBIR0
Contract Number (US Government)
FA8649-23-P-00560
Award Phase
Phase II0
Award Amount (USD)
1,249,9890
Date Awarded
November 30, 2022
0
End Date
August 30, 2024
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Abstract

The primary objective of this proposed effort is to improve the Resilient Information Sharing capability of United States warfighters against potential near-peer adversaries using enhanced space-based intelligence, surveillance and reconnaissance (ISR) assets.  The enhancements seek to improve the security and resiliency of orbital assets through a novel cybersecurity paradigm and laser-based communications embedded in flocks of low-size, weight, power and cost (SWaP-C) picosats.  These enhancements will be applied to commercial products used in a Space-Data-as-a-Service (SDaaS) offering, undergo terrestrial and orbital test and evaluation (T&E) culminating with hands-on validation by enlisted End-Users. The solution leverages low SWaP-C picosatellite platforms to achieve dedicated and persistent ISR from orbit, thereby identifying the most timely, relevant and important information collected.  The solution involves space payloads containing sensor systems that perform both earth and orbital observation to detect electronic signals and conduct observations using synthetic aperture radar as well as electro-optical sensors approaching one meter per pixel resolution by leveraging multiple satellites in a flock.  The payloads are at the picosatellite (“PocketQube”) size, which is nominally 5cm square.  These picosatellites contain custom software and algorithms that use low-resource (machine) learning (LRL) techniques to automatically identify Objects and Activities that fall outside the "normal" pattern of life observed by the picosatellite constellation in real time.  The effort and time required to train machine learning models using LRL is significantly less than other known approaches.  Pattern of life is maintained on the Objects and Activities, and anomalous Objects and Activities are relayed to Ground Stations and are readily integrated into other data systems to improve situational awareness.  This pattern of life and anomaly detection (POL/AD) capability is fully supported on Space Force classified networks in the Offeror’s commercial off-the-shelf (COTS) software product Watchman for Defense (W4D), which is readily adaptable for application on space-based assets.

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