The Navy is seeking a Direct to Phase II SBIR solution to develop a portable AI/ML-enabled diagnostic module for in-field troubleshooting of avionics optical networks. The effort focuses on designing, building, and validating a plug-in module that works with existing Optical Backscattering Reflectometer and Optical Time Domain Reflectometer mainframes to improve fault detection and maintenance support. Key requirements include ruggedized support-equipment performance, compatibility with multiple fiber types and wavelengths, centimeter-scale spatial resolution, and system-level testing to support TRL 6 entry criteria. The topic is pre-release, opens May 6, 2026, and closes June 3, 2026, with proposals due by 4:00 PM ET.

## Description

Current airborne military (mil-aero) core avionics, electro-optical (EO), communications, and electronic warfare systems are experiencing continuous growth in bandwidth demand, coupled with stringent requirements to reduce Size, Weight, and Power (SWaP). Earlier-generation multimode optical fibers have replaced traditional shielded twisted-pair wire and coaxial cable, offering increased electromagnetic interference (EMI) immunity, higher bandwidth and throughput, and notable reductions in aircraft size and weight.&nbsp;However, maintenance and troubleshooting of these advanced optical networks remain highly dependent on traditional telecommunication test equipment. Identifying and resolving faults—such as fiber breaks, fractures, and high-loss terminations—requires locating and distinguishing anomalies within meter-level precision, whereas modern avionic information-processing networks demand centimeter-level spatial resolution from source to detector.&nbsp;Fault detection must extend beyond typical Weapons Replaceable Assembly (WRA) interfaces to identify:<ul><li>Backplane/module degradation</li><li>Line replaceable module-to-optical transceiver faults</li><li>Polymer waveguide failures</li><li>Inline sensor (fiber grating) issues</li><li>Optical link loss across concatenated waveguide segments</li></ul>&nbsp;Frequent airframe panel removal during fault isolation disrupts aircraft availability and mission readiness—especially for stealth platforms—highlighting the need for faster, more accurate, and less intrusive diagnostics.&nbsp;To overcome these limitations, a portable AI/ML-enabled troubleshooting device is proposed to support field diagnostics across military airborne fiber-optic systems. The device will leverage next-generation reflectometry technologies and machine intelligence to enhance fault resolution precision and technician efficiency.&nbsp;Key Capabilities:<ul><li>AI-Augmented Fault Detection<ul><li>Real-time identification of defects (breaks, voids, misalignments, link degradation)</li><li>Pattern recognition and anomaly classification using historical signature databases</li></ul></li><li>AI-Driven Virtual Assistants<ul><li>On-device or network-connected chatbots providing guided maintenance workflows</li><li>Embedded AR interface for overlaying diagnostics on test hardware in real time</li></ul></li><li>Advanced Troubleshooting Metrics<ul><li>Spatial resolution to centimeter scale across multiple fiber types</li><li>Predictive maintenance algorithms to reduce unplanned network downtime</li></ul></li><li>Plug-and-Play Integration<ul><li>&nbsp;Fully compatible with existing portable OTDR/OBR mainframes</li><li>Support for both multimode (50/125, 62.5/125, 100/140 µm) and single mode (9/125 µm) fiber types</li><li>GUI developed for intuitive field use across all operational conditions</li></ul></li><li>Wavelength and Environmental Resilience<ul><li>Operational wavelength support: SWDM and CWDM</li><li>Designed for MIL-PRF-28800 Class 2 with select Class 1 enhancements</li><li>Operational temperature range: –40°C to +95°C</li><li>Resistant to mechanical shock, altitude variation, vibration, humidity, and thermal cycling</li></ul></li></ul>The device will build upon a fusion of legacy and emerging fiber-optic diagnostic technologies, including:<ul><li>Optical Time Domain Reflectometry (OTDR)</li><li>Optical Backscatter Reflectometry (OBR)</li><li>Photon-Counting OTDR (PC-OTDR)</li><li>Low Correlation OTDR (LC-OTDR)</li><li>Pseudo Random Sequence (PRS) Correlation OTDR (C-OTDR)</li><li>Optical Frequency Domain Reflectometry (OFDR)</li></ul>

## Objective

Design, develop, and integrate a portable artificial intelligence/ machine learning (AI/ML)-enabled diagnostic module compatible with existing Optical Backscattering Reflectometer (OBR) and Optical Time Domain Reflectometer (OTDR) mainframes. The module will be engineered to support in-field optical network troubleshooting and management for high-speed communication systems.

## Phase I

For a Direct to Phase II topic, the Government expects that the small business would have accomplished the following in a Phase I-type effort and developed a concept for a workable prototype or design to address, at a minimum, the basic requirements of the stated objective above. The below actions would be required to satisfy the requirements of Phase I:Concept Development: Developed a concept for a viable prototype or design solution that addresses, at a minimum, the core technical and performance objectives outlined in the stated topic.Feasibility Demonstration: Designed, developed, and demonstrated the technical feasibility of a low-cost, AI/ML-based plug-in module compatible with portable OBR and OTDR mainframes. The solution must meet applicable aviation support equipment requirements, including ruggedization, thermal compatibility, and interface standards.Performance Modeling and Simulation: Modeled and simulated the plug-in module&rsquo;s performance under high-speed application conditions, validating its functionality across relevant operational scenarios and wavelengths.Design Packaging: Delivered a conceptual packaged design of the plug-in module, incorporating mechanical footprint, connector interface, and Graphical User Interface (GUI) considerations to support seamless integration into current field-deployable test equipment.FEASIBILITY DOCUMENTATION: Offerors interested in participating in Direct to Phase II must include in their response to this topic Phase I feasibility documentation that substantiates the scientific and technical merit and Phase I feasibility described in Phase I above has been met (i.e., the small business must have performed Phase I-type research and development related to the topic, but from non-SBIR funding sources) and describe the potential commercialization applications. The documentation provided must validate that the proposer has completed development of technology as stated in Phase I above. Documentation should include all relevant information including, but not limited to technical reports, test data, prototype designs/models, and performance goals/results. Work submitted within the feasibility documentation must have been substantially performed by the offeror and/or the principal investigator (PI). Read and follow all of the DON SBIR FY26 Release 3 Direct to Phase II Broad Agency Announcement (BAA) Instructions. Phase I proposals will NOT be accepted for this topic.

## Phase II

Design, construct, and validate a functional AI/ML-enabled plug-in module prototype. Focus on transitioning the concept design into an operational system capable of meeting the rigorous demands of military optical diagnostics.&nbsp;Include in the Prototype Design and Fabrication the following:<ul><li>Engineering of a robust plug-in module design based on Phase I feasibility studies and modeling outcomes.</li><li>Integrating AI/ML processing hardware, signal acquisition architecture, and interfaces into a fully packaged prototype.</li><li>Ensuring form-factor compliance with portable OTDR and OBR mainframes, including connector integrity, mechanical footprint, and GUI usability.</li><li>Compiling system-level test data and validating against entry criteria for Technology Readiness Level (TRL) 6.</li></ul>

## Phase III: Dual Use Applications

Collaborate with defense avionics industries as well as support equipment companies to accelerate transition to production.Commercial telecommunication systems, fiber-optic networks, and data centers will benefit from the development of the AI/MIL based OBR and OTDR. These applications will be able to easily test/diagnose optical networks.

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The DON26BZ01-DV003 project seeks to develop a portable AI/ML-enabled diagnostic module for in-field troubleshooting of avionics optical networks. This 

DIRECT TO PHASE II: AI/ML Assisted Field Troubleshooting in Avionics Optical Network

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