Program Description:
The U.S. Armed Forces have been at the forefront of technological
development, but with the advancements across the world in radar and weapon technology,
adversaries have closed the gap. U.S. aircraft are increasingly required to operate in hostile
environments heavily defended by integrated air-defense systems (IADS). Current IADS consist
of radars that employ active electronically scanned array (AESA) antennas and individual
missiles equipped with sensors that operate in either the radio frequency (RF), electro-optic (EO)
or infrared (IR) bands. The next evolution of advanced IADS is likely to employ radars, surfaceto-air (SAM) and air-to-air (AAM) threats that utilize multi-spectrum technology. That is, U.S.
aircraft will be expected to counter IADS equipped with missiles guided by sensors that operate
with various levels of coordination between the EO/IR and RF regimes for detection, navigation,
and/or tracking processes, i.e., multi-spectrum threats. As IADS advance towards employing
multi-spectrum capabilities, a foreseeable problem is the relevancy and effectiveness of
countermeasures (CMs) developed for a single spectrum when they are employed against a
multi-spectrum threat. In addition, various emerging advanced threats may pose serious risk to
high-value airborne assets (HVAA). HVAA are necessary for mission success and are
historically provided natural protection due to their operational distance being far enough outside
the immediate battlefront. With adversaries understanding the importance of HVAA to mission
success, as well as their vulnerabilities, HVAA may become a likely target for emerging longerrange advanced threats. Continued HVAA Protection is key for the AF to maintain air
superiority and satisfy their core mission goals (e.g., Intelligence, surveillance, and
reconnaissance (ISR), Global Reach, command and control (C2), etc.).
In order for a system to operate in a peer or near-peer conflict a degree of cognition, system
integration, artificial intelligence and machine learning (AI/ML) are required to generate and/or
maintain a competitive advantage due to the sheer volume of data, speed of activity, and
complexity of threat capabilities. Cognitive typically refers to systems or processes that attempt
to mimic or replicate the human decision-making by utilizing sensors, perceptions, learning,
reasoning, and memory autonomously. AI/ML are the tools and processes used to teach systems
to make those human-like decisions. The Cognitive Electronic Warfare (EW) ecosystem
encompasses all the AI/ML utilized by systems and processes to enable varying levels of
autonomy across the various EW missions, with AFRL supporting the warfighter and ops
community missions including (rapid) reprogramming on the ground to closed-loop self-protect
jamming in the fight. In the laboratory or squadron, AI/ML can assist with data analysis or
mission data generation to increase the volume of data processed and the confidence in the data
to be used operationally. This will allow for next sortie updates to mission data while generating
reports for further analysis. Data collected during in-field and/or in-fight operations will be
critical to further development in system and process learning. As AI/ML advances it will be
able to process and utilize larger amounts of data in real-time, opening the potential for
autonomous EW on an asset. This ability to have the capability for on-board data collect and
analysis will be necessary for future closed-loop real-time autonomous reactions to changes or
unknowns in the perceived Electromagnetic Spectrum (EMS) environment (i.e., closed-loop
sensing and self-protect jamming). Essential for rapid insertion and assessment of AI/ML
technology are the adoption of open standards, agile Devops /algorithm development, and
process validation tools environments. This AI/ML foundation will be critical to allowing
AI/ML to migrate into fielded systems and eventually onto assets for closed loop operations.
Project Kaiju will be a one-step, Closed BAA with the following nine (9) main tasks and
technical objectives:
(Gamera) Big Data for Cognitive EW (CEW) Research: Conduct a study that investigates
which key community developed tools should be integrated into a common and modular
framework to generate “Big Data”.
(King Ghidorah) Software-Defined Radio (SDR) Research: SDR code library requires indepth investigation of target system hardware for the purpose of understanding its operation for
the purpose of creating the best possible SDR emulation of the target system. Procurement of
target systems, development of data links to interface with SDRs and other equipment to
command and control systems, disassembly (to include destructive testing) of target systems, lab,
field and flight testing, and procurement of candidate SDR hardware/software.
(Mecha Rodan) Multi-Spectrum Threat Defeat: Refine existing Multi-Spectrum M&S
environments to add advanced capabilities (including model accuracy) and spans across
Electronic Support (ES) and Electronic Attack (EA).
(Kumonga) RAPTURE Laboratory: Design, fabricate, test, and document special purpose
hardware to meet research and development (R&D) test requirements for Size, Weight, and
Power (SWAP) constrained program requirements. Perform lab and field testing of custom
designed hardware (includes soldering surface mount Printed Circuit Board (PCB)’s, modifying
PCB’s, assembling custom cables, computer aided design (CAD) of custom enclosures, and
assembly of the final product).
(Mothra) EA Demo: Build a reconfigurable EA processing framework for assessment of EA
capabilities (emitter tracking, technique selection, technique generation).
(King Kong) Real Time Algorithm Development: Utilize government furnished hardware
architecture description to determine the viability of government furnished non-real-time
machine learning algorithms for real-time applications.
(Baragon) RF EW Demonstrator (REWD) for Next Sortie Mission Data Reprogramming:
Develop, mature, and evaluate advanced EW algorithmic concepts to detect, sort, identify,
disambiguate, and track complex emitters in complex environments. This includes leveraging
algorithms developed in Defense Advanced Research Projects Agency (DARPA)’s Adaptive
Radar Countermeasures (ARC), AFRL’s Electronic Support Critical Experiment (ESCE), and
ONR’s Reactive Electronic Attack Measures (REAM). This effort will also include integrating
multi-sourced processing chains, closed loop software component control and tuning, component
performance comparison, analysis and visualization products to aid human/machine teaming and
trust.
(Colossus) Advanced Threat Defeat (ATD): Develop novel and cognitive electronic warfare
capabilities to generate multi-layered EA techniques resulting in long range kill webs. Leverage
distributed sensing, machine learning and AI and align with Advanced Battle Management
System (ABMS) concepts for autonomous vehicles to enable Joint All Domain Command and
Control (JADC2) resulting in coordinated joint fires and convergence of EW effects.
(Godzilla) Program Management: Perform program management of scope, schedule, cost, and
risk for the overall contract and for each individual research project and development activity.
Additionally, this BAA will address the issues both individually and collectively. No R&D
conducted under this program will be done in isolation, but rather in full consideration of how
the new technologies can progress toward full integration with large, complex systems, ready to
transition to support of the warfighter.
The contractor will be expected to deliver all software and/or hardware as required, and all
associated data developed and/or used in the execution of this effort, unless clearly stated
otherwise in the proposal. It is anticipated that a BAA providing more details into this program
will be released January 2022; the solicitation will be made available at
http://www.beta.sam.gov.
Direct any questions to the Contracting or Technical point of contacts identified in the
announcement.
total maximum value of $23,764,751,000
