OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Develop next-generation autonomous tracking to increase the capability and scope of utility of Unmanned Aerial Vehicles (UAVs), while decreasing the level of remote human operator involvement. DESCRIPTION: The Air Force's Operational Imperatives 3 (Moving Target Engagement) and 4 (Tactical Air Dominance) have led the AFRL Sensor Directorate's increased efforts toward autonomy capabilities research, development and transition for current and future unmanned platforms. This work is focused on enhancing smaller, more numerous, and distributed capabilities as a complement to larger, more powerful, and proven capabilities, intelligent machines that can adapt in unstructured and contested environments at machine speed given overwhelming data and are attritable when necessary, and decision superiority achieved through ubiquitous and persistent data collection, situational understanding at the edge and support to a robust information warfare capability. This SBIR Topic is focused on increasing the capability of Unmanned Aerial Vehicle (UAV) autonomy and increasing the scope of utility of UAVs by enabling autonomy to be used in a much greater number of situations than it can be today for a much greater variety of tasks. A critical component of autonomous technology is sensor fusion, which addresses the Find-Fix-Track portion of the kill chain. In environments where air-to-air passive sensing is utilized, inaccurate tracks and false associations can introduce significant uncertainty in the situational awareness picture. Novel approaches to enhancing air target tracking from passive sensor data have the potential to reduce this uncertainty, enabling better-informed and more reliable autonomous decision-making when tracking and engaging air targets. Due to both the proposed size (Group 2/3 UAVs) and forward environments, very low bandwidth and intermittent communications are expected with a high likelihood of extended periods of time with no communication and substantial limitations in situational awareness. This necessitates highly capable autonomous control software on these platforms that can make decisions without the involvement of a human operator, using incomplete, uncertain and sometimes inaccurate information. AFRL has proven mature autonomy software on a variety of platforms, such as part of the Skyborg program. Specific advancements are needed in autonomy's ability to handle complex tasks and dynamic, unstructured and uncertain environments. Autonomy must be able to perform more complex tasks than currently possible, for example, to maneuver throughout a congested airspace, including uncooperative or adversarial vehicles. One key area that requires further enhancement is optimizing the performance of a distributed sensor network, including fusing of passive sensor data to include improved target localization and disambiguation; however, the team will consider advancements in other key areas such as analytics or perception management (i.e. prediction, situation assessment). PHASE I: As this is a Direct-to-Phase-II (D2P2) topic, no Phase I awards will be made as a result of this topic. To qualify for this D2P2 topic, the Air Force expects the applicant(s) to demonstrate feasibility by means of a prior Phase I-type effort that does not constitute work undertaken as part of a prior or ongoing SBIR/STTR funding agreement. The feasibility study must show that the applicant(s) understands the current state of the art in UAV autonomous sensing and explain how the proposed approach will advance the state of the art. The feasibility study must describe in detail the applicant(s)'s concept for autonomous sensing for UAVs. The feasibility study should clearly explain the rationale for the selection of the proposed concept for next-generation autonomy. This rationale must be clearly supported by, for example, analysis, testing in simulation, and/or small scale-model testing. Approaches to next-generation autonomous sensing that are adapted from systems other than UAVs are of interest and approaches that leverage previous research in this area are also of interest. The feasibility study should describe the approach to testing of the next-generation autonomy algorithms. The feasibility study must provide a clear explanation of the feasibility of the proposed testing methodology. PHASE II: Using results from the Phase I-type effort, the Phase II effort will develop, demonstrate and validate prototype Next-generation Autonomy software for UAVs that addresses the requirements and constraints stated in the Description section above. The Phase II award shall address, at a minimum: 1) Development of algorithms and software capable of addressing the requirements provided in the Description section of this Topic. 2) The Government anticipates that testing to support development of the Next-generation Autonomy software will consist of, at a minimum, simulation-based testing and potentially small scale flight testing. Testing with full scale UAVs is costly and time-consuming and is not a requirement for a successful proposal. 3) Validation of Next-generation Autonomy software will also be accomplished via simulation. The validation phase of the project will consist of structured testing against metrics that the small business will develop as part of this project. The validation phase will elucidate the exit TRL of the Next-generation Autonomy software resulting from this project. 4) Deliverables shall include the Next-generation Autonomy software, a report containing robust documentation of the software (including algorithms, architecture, interfaces, build instructions, necessary software components and environment to build the Next-generation Autonomy software and a software user manual) data acquired during this project, and test methodology and metrics and test results. 5) Composition of the proposed team: teams that are structured to facilitate knowledge transfer of previous research results to this project, for example a small business-university team, while not required, are strongly encouraged. 6) Describe how the software is architected to address cyber security issues and the approach. Approaches to perception and communications are not in scope of this Topic, although assumptions should be stated regarding perception and communications capability on the UMV PHASE III DUAL USE APPLICATIONS: The awardee(s) will identify potential commercial and dual use applications for this technology. This can include areas where precision tracking using passive sensor data is necessary. These technologies include aircraft, ground vehicle, and maritime tracking. REFERENCES: 1. Vakil, A., Liu, J., Zulch, P., Blasch, E., Ewing, R. and Li, J., 2020, March. Feature level sensor fusion for passive RF and EO information integration. In 2020 IEEE Aerospace Conference (pp. 1-9). IEEE. 2. Mallick, M., Chang, K.C., Arulampalam, S. and Yan, Y., 2019. Heterogeneous track-to-track fusion in 3-D using IRST sensor and air MTI radar. IEEE Transactions on Aerospace and Electronic Systems, 55(6), pp.3062-3079. 3. Fu, L., Shi, Y., Peng, D. and Ullah, I., 2024. Unified framework for multi-sensor distributed fusion with memory configuration. Aerospace Science and Technology, p.109184. 4. Bucci, D.J. and Varshney, P.K., 2019, July. Decentralized multi-target tracking in urban environments: Overview and challenges. In 2019 22th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. 5. Vakil, A., Liu, J., Zulch, P., Blasch, E., Ewing, R. and Li, J., 2021. A survey of multimodal sensor fusion for passive RF and EO information integration. IEEE Aerospace and Electronic Systems Magazine, 36(7), pp.44-61. KEYWORDS: sensor fusion; Find-Fix-Track; Target Tracking;
