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FA945322CA106

Definitive Contract

Overview

Government Description
LEARNING ASSISTED SINGLE SATELLITE RAPID GEOLOCATION OF GROUND-BASED EMI SOURCES
Place of Performance
Germantown, MD 20876 United States
Pricing
Fixed Price
Set Aside
Small Business Set Aside - Total (SBA)
Extent Competed
Full And Open Competition After Exclusion Of Sources
Est. Average FTE
2
Related Opportunity
None
Intelligent Fusion Technology was awarded Definitive Contract FA945322CA106 (FA9453-22-C-A106) for Learning Assisted Single Satellite Rapid Geolocation Of Ground-Based Emi Sources worth up to $749,975 by Air Force Research Laboratory in August 2022. The contract has a duration of 2 years 3 months and was awarded through SBIR Topic Single Satellite Rapid Geolocation of Satellite Communications (SATCOM) Electromagnetic Interference (EMI) with a Small Business Total set aside with NAICS 541715 and PSC AR12 via direct negotiation acquisition procedures with 4 bids received.

SBIR Details

Research Type
Small Business Innovation Research Program (SBIR) Phase II
Title
Learning Assisted Single Satellite Rapid Geolocation of Ground-based EMI Sources
Abstract
Satellite communications (SATCOM) are facing increasingly diverse physical and electromagnetic interference (EMI) that transmit radio frequency (RF) signals in X/Ku/K/Ka/Q-bands. Interference of satellite communications is a frequent and ongoing concern for both DoD and civilian enterprises. Geolocation of the interfering source is an essential step in mitigating or eliminating the interference and restoring operation of the communications service. In this Phase I project, the Intelligent Fusion Technology, Inc. (IFT) team has developed a rapid and passive single-satellite based 3D meter-level geolocation for ground EMI sources that interfere with the uplinks of SATCOM. The Phase I effort has resulted in a prototype of the proposed blind Doppler estimation and constrained unscented Kalman filter (cUKF) based SSG. In Phase II, IFT team will refine and expand the Phase I technologies to integrate context-aware ML/AI. Context-aware geolocation will be developed to incorporate contextual information of the satellite as well as the potential EMI sources. Deep learning techniques will be incorporated in the SSG framework to predict the optimal design parameters for the blind carrier Doppler estimation and cUKF. The phase II prototype with fully integrated ML/AI enhancements is expected to obtain the meter-level geolocation accuracy.
Research Objective
The goal of phase II is to continue the R&D efforts initiated in Phase I. Funding is based on the results achieved in Phase I and the scientific and technical merit and commercial potential of the project proposed in Phase II.
Topic Code
AF193-015
Agency Tracking Number
F193-015-0216
Solicitation Number
19.3
Contact
Yingli Wu

Status
(Complete)

Last Modified 8/11/22
Period of Performance
8/11/22
Start Date
11/21/24
Current End Date
11/21/24
Potential End Date
100% Complete

Obligations
$750.0K
Total Obligated
$750.0K
Current Award
$750.0K
Potential Award
100% Funded

Award Hierarchy

Definitive Contract

FA945322CA106

Subcontracts

0

Activity Timeline

Interactive chart of timeline of amendments to FA945322CA106

People

Suggested agency contacts for FA945322CA106

Competition

Number of Bidders
4
Solicitation Procedures
Negotiated Proposal/Quote
Evaluated Preference
None
Commercial Item Acquisition
Commercial Item Procedures Not Used
Simplified Procedures for Commercial Items
No

Other Categorizations

Subcontracting Plan
Plan Not Required
Cost Accounting Standards
Exempt
Business Size Determination
Small Business
Defense Program
None
DoD Claimant Code
Missile And Space Systems
IT Commercial Item Category
Not Applicable
Awardee UEI
YAJJH8KCTF51
Awardee CAGE
6B0K6
Agency Detail
Awarding Office
FA9453 FA9453 AFRL RVK
Funding Office
F4FBEQ
Created By
jeffery.martinez
Last Modified By
brenda.hamilton@sa5700.fa9401
Approved By
brenda.hamilton@sa5700.fa9401

Legislative

Legislative Mandates
None Applicable
Performance District
MD-06
Senators
Benjamin Cardin
Chris Van Hollen
Representative
David Trone
Modified: 8/11/22