Synthetic RF Training Data Generation

DEPARTMENT OF THE ARMY SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM SBIR 21.4 Broad Agency Announcement (BAA) Army Applied SBIR Opportunity (ASO) Announcement November 16, 2021: ASO issued for pre-release November 30, 2021: Army begins accepting proposals January 4, 2022: Deadline for receipt of proposals no later than 12:00 p.m. ET IMPORTANT Deadline for Receipt : Proposals must be completely submitted no later than 12:00 p.m. ET, January 4, 2022. Proposals submitted after 12:00 p.m. will not be evaluated. The final proposal submission includes successful completion of all firm level forms, all required volumes, and electronic corporate official certification. Classified proposals will not be accepted under the DoD SBIR Program. This BAA and the Defense SBIR/STTR Innovation Portal (DSIP) sites are designed to reduce the time and cost required to prepare a formal proposal. The DSIP is the official portal for DoD SBIR/STTR proposal submission. 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All proposals in response to the technical area(s) described herein will be submitted in accordance with the instructions provided under 21.4, found here: https://beta.sam.gov/opp/b79ded14dcf54451bcfb11bddf5cd259/view?keywords=%22army%20sbir%22&sort=-relevance&index=opp&is_active=true&page=1. a. Eligibility The eligibility requirements for the SBIR/STTR programs are unique and do not correspond to those of other small business programs. Please refer to Section 3.1, Eligible Applicants, of BAA 21.4 for full eligibility requirements. b. Anticipated Structure/Award Information Phase I Please refer to Section 1, Funding Opportunity Description, provided in BAA 21.4 for detailed information regarding SBIR/STTR phase structure and flexibility. For this BAA, the Department of the Army will accept Phase I proposals for the cost of up to $250,000 for a 6-month period of performance. 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Proposers should refer to Section 4, Application and Submission information, of BAA 21.4 for detailed proposal preparation instructions. Proposals that do not comply with the requirements detailed in BAA 21.4 and the research objectives of this ASO are considered non-conforming and therefore are not evaluated nor considered for award. Phase II proposals shall not exceed 10 pages. Phase II commercialization strategy shall not exceed 10 slides. This should be the last section of the Technical Volume and will not count against the 10-page limit. Please refer to Appendix A of BAA 21.4 for detailed instructions on Phase II proposal preparation. c. Evaluation of Proposals Section 5, Evaluation of Proposals, in BAA 21.4 provides detailed information on proposal evaluation and the selection process for this ASO. d . Discretionary Technical and Business Assistance (TABA) Participation in the Army applied SBIR TABA program is voluntary for each Army Applied SBIR awardee. Services provided to Army Applied SBIR firms under the auspices of the TABA program may include, but are not limited to: Access to a network of scientists, engineers, and technologists focused on commercialization and transition considerations such as protected supply chain management, advanced manufacturing, process/product/production scaling, etc; Assistance with intellectual property protections, such as legal considerations, intellectual property rights, patent filing, patent fees, licensing considerations, etc; Commercialization and technology transition support such as market research, market validation, development of regulatory or manufacturing plans, brand development; Regulatory support such as product domain regulatory considerations, regulatory planning, and regulatory strategy development. Vendors. The Army will select a preferred vendor for the Army Applied SBIR TABA program through a competitive process. Alternately, a small business concern may, by contract or otherwise, select one or more vendors to assist the firm in meeting the goals listed above. The Applicant must request the authority to select its own TABA provider in the Applied SBIR proposal, demonstrating that the vendor is uniquely postured to provide the specific technical and business services required. Participation. Participation in the Army Applied SBIR TABA program is voluntary for each Army Applied SBIR awardee. If a small business concern selects their own vendor, they must include the request in the Applied SBIR proposal. If a small business concern opts to use the Army preferred vendor, the firm may opt into the program at any time during execution of the SBIR project. Resources. The Applied SBIR program sponsors participation in the TABA program. The resource limitation for each firm is: Phase I Firms: Up to $6,500 per project per year (in addition to the base SBIR award amount); Phase II Firms: Up to $50,000 per project; Army-Preferred Vendor: In addition to the base SBIR award amount; Firm-Selected Vendor: Included in the base SBIR award amount and must be included in Phase II proposal. e. Due Date/Time Full proposal packages (Proposal Cover Sheet, Technical Volume, Price/Cost Volume, and Company Commercialization Report inclusive of supporting documentation) must be submitted via the DoD SBIR/STTR Proposal Submission website per the instructions outlined in BAA 21.4 Section 4.3 Electronic Submission no later than 12:00 p.m. ET, January 4, 2022. Army SBIR 21.4 Topic Index A214-046 Synthetic RF Training Data Generation A214-046 TITLE: Synthetic RF Training Data Generation OBJECTIVE: The purpose of this topic is to establish a comprehensive RF-based database that will be used to train a deep learning computer vision algorithm for a target detection system. This data would be complementary to existing data and is required due to the Deep Learning approach. DESCRIPTION: The purpose of this topic is to establish an RF-dataset to train deep learning algorithms for proximity and RF seeking target detection algorithms. Currently, we either collect real world data, which is very expensive and time consuming, or we utilize modeled inputs. The method of modeling inputs utilizes Gaterrayed software, which requires intensive labor hours of manual work. AI/ML deep learning approaches do not yet exist for our RF proximity fuse & seeker applications. However, research has show that datasets comprised of both real-data and synthetically generated data lead to superior performance of deep learning algorithms. This approach would allow us to create additional datasets based on the real datasets we already have. It will cost less and create enough variation to train the Proximity Target Detection algorithms. Target detection models allow proximity fuses and RF seekers to distinguish targets from background clutter. AI/ML techniques show promise in achieving advanced capabilities, but require data with variable background and operating conditions. Large data sets with a wide range of variable backgrounds can be achieved through synthetic data generation methods. Establishing the large number of samples of data through testing and experimentation are both cost and time prohibitive. Instrumentation to capture all of the salient features in the data is also intricate and requires significant resources at testing sites. Generating variations on data would increase the RF dataset to the point that training an advanced AI algorithm is feasible. PHASE I: Direct to Phase 2 requires demonstration of AI/ML computer vision algorithms using synthetically generated data specific to target detection and recognition, including pertinent data and report(s). Any development with RF techniques is a plus but not required for Phase I. PHASE II: Phase II should consist of utilizing RF data to develop variation algorithms pertaining to the future commercialization of this topic. This phase will also consist of training the RF deep learning algorithms with synthetic data and integrating into a proximity fuze. Must be able to demonstrate performance. PHASE III: Phase III, commercialization, must consist of generating large data sets for testing. Training algorithms with increased synthetic datasets & transition to other proximity fuzes (i.e. MOFA II or LR- PGK) must also occur. KEYWORDS: RF Data; deep learning; algorithms; machine learning; automated intelligence; REFERENCES: Press, Gil. “Andrew Ng Launches a Campaign for Data-Centric AI”. 16 June, 2021. Forbes.com https://www.forbes.com/sites/gilpress/2021/06/16/andrew-ng-launches-a-campaign-for-data-centric-ai/?sh=7b78fe5574f5