Inactive

Advanced Object Classification (AOC) 1.1 is a software upgrade for Upgraded Early Warning Radars (UEWR) that will improve classification of objects in the midcourse phase of ballistic missile flight. ... Advanced Object Classification (AOC) 1.1 is a software upgrade for Upgraded Early Warning Radars (UEWR) that will improve classification of objects in the midcourse phase of ballistic missile flight. AOC 1.1 will build off improvements made to UEWR classification achieved through development and fielding of AOC 1.0. While testing and fielding AOC 1.0, the Government identified minor algorithm opportunities as well as Interface Control Document-driven updates that must be incorporated into AOC 1.1. Additionally, the Government envisions AOC 1.1 will have a flat-file classification database that can be re-configured without re-compiling software changes. This capability will enable AOC rapid reconfiguration to address emerging threats without requiring the Government to return to the developer for software updates, assessments, or other modifications. AOC Overview: Contains 37 algorithms increasing object classification accuracy against complex threats with countermeasures Uses machine learning to improve overall classification results Requires no change to radar hardware, waveforms, pulse scheduling, or Ground-based Midcourse Defense (GMD) Fire Control (GFC) interface message format Core Capabilities: AOC 1.1 will incorporate additional algorithm updates and enhancements beyond AOC 1.0 to further improve object classification accuracy and system performance. REQUESTED INFORMATION: Respondents to this RFI are invited to prepare and submit the following: Describe your experience with threat object classification for ballistic missile defense during the midcourse phase of flight, specifically as it relates to Radio Frequency (RF) phenomenology. In your response, discuss the types of RF features you have analyzed or exploited to distinguish between objects. Additionally, elaborate on the classification algorithms you have developed or implemented, the types of data (simulated or measured) you have worked with, and any challenges you have encountered in applying RF phenomenology to real-world or simulated scenarios. If applicable, include your experience with countermeasure discrimination, system integration, or field testing in this context. Describe your experience in the design, development, or enhancement of phased array radar systems operating in the ultrahigh frequency (UHF) band. In your response, detail your involvement in system architecture, RF front-end design, and digital or analog beamforming techniques. Discuss any integration work you have done with larger radar and/or command and control systems. Describe your experience developing algorithms or applications in Matlab, including any work you have done to transpile or convert Matlab code into other programming languages (e.g., C, C++, Python, etc.) for deployment in operational environments. In your response, discuss the tools, methodologies, or best practices you have used to ensure code accuracy, performance, and maintainability during the transpilation process. Additionally, elaborate on your experience collaborating with other prime contractors or external teams to integrate your code into existing complex codebases. Include examples of how you addressed challenges related to code compatibility, interface definition, version control, and testing in a multi-contractor environment. Describe your experience developing and deploying artificial intelligence (AI) or Machine Learning (ML) solutions that operate and deliver results in real-time environments. In your response, detail the types of AI/ML models you have designed or implemented (e.g., neural networks, decision trees, clustering algorithms, etc.) and the nature of the data you worked with. Discuss specific real-time requirements or constraints you had to meet (e.g., latency, throughput, resource limitations) and the tools, frameworks, or platforms you used (e.g., TensorFlow, PyTorch, embedded systems, etc.). Explain how you optimized your models and system architecture to achieve reliable, low-latency performance. Additionally, describe any challenges you encountered in integrating AI/ML components into operational system (including data pipeline design, model inference speed, or system robustness) and how you addressed them to ensure successful real-time delivery. Because one of the objectives of this RFI is to determine the capability of small businesses to meet the requirements contained herein, and to inform the acquisition strategy with regard to setting aside any future acquisitions for small businesses, MDA requests the following information: The prime small business contractor responding to this RFI must include its past experience in managing subcontractors on similar requirements, and if applicable, include subcontractor or teaming partners past experience applicable to requirements specified in this Sources Sought. Responsible small businesses planning teaming arrangements to meet the support requirements listed above are expected to articulate the portions of work that the prime small business intends to perform as well as articulate the portions that subcontractors or teaming partners (if applicable) will perform. Responses must include business size and business socio-economic category of all subcontractors or teaming partners identified to perform work associated with the NAICS for this Sources Sought. SUBMISSION REQUIREMENTS: Responses are not to exceed 10 total pages (including cover page and table of contents). A respondent’s information/documents submitted in response to this RFI will not be returned. Responses shall be submitted in Microsoft (MS) Word compatible format or electronic Portable Document Format (.pdf) only, with searchable capability (no scanned documents), capable of being printed on 8.5x11 inch paper with one inch margins (top, bottom, left, and right), single-line spaced, Times New Roman font, and no smaller than 12-point font. Any drawings shall be capable of being printed on 11x17 inch paper and no smaller than 10-point font. All figures and drawings are included in the page limits. All responses shall contain a Cover Page identifying: RFI Title Company or organization name, mailing address (City/State/Zip), and Unique Entity Identifier Company web page URL, if applicable Point of Contact (name, title, phone number, and e-mail address) Unclassified submissions should be delivered via DoD’s Safe Access File Exchange (SAFE) website at https://safe.apps.mil/. Since unauthenticated users (Non-DoD Personal Identity Verification (PIV) certificates) must receive a drop-off request from a DoD user, please e-mail the Contracting Officer and Contract Specialist of your company’s intent to deliver a response. You will be provided with a “Drop-off Request,” which you will use to submit your files. The Drop-Off Request will be valid for 14 days. For additional guidance, please see the SAFE website at https://safe.apps.mil/. To request classified delivery instructions, interested parties shall notify the Government via the unclassified email address below within 10 days of this notice posting if classified information will be delivered as part of the response and identify the classification level of the submission. The Government will provide specific delivery instructions depending on the identified classification level of information. Telephone inquiries will not be accepted or acknowledged, and no feedback or evaluations will be provided to companies regarding submissions. Responses to this RFI are requested by close of business on Friday, October 17, 2025. All responses shall be reviewed to ensure correct security classification and consideration of operational sensitivities. To the maximum extent possible, submit nonproprietary information. Any proprietary information submitted should be identified as such and will be handled accordingly and protected from disclosure. Proprietary information will be safeguarded in accordance with the applicable Government regulations. The Government shall not be liable for damages related to proprietary information that is not properly identified. Any submissions in response to this RFI constitutes consent for that submission to be reviewed by Government personnel, Federally Funded Research and Development Center (FFRDC) Contractor employees, and Advisory & Assistance Services (A&AS) Contractor employees supporting MDA/GMS who have signed Non-Disclosure Agreements (NDA) - unless the respondent clearly objects in writing to the release of this information to FFRDC and/or A&AS Contractor employees in a cover letter accompanying the respondent’s submission. All Government and DoD contractor personnel reviewing RFI responses understand their responsibility for proper use and protection from unauthorized disclosure of proprietary information. All correspondence related to this matter should be e-mailed to the points of contact listed in this RFI.