Rapid Data and Sensor Fusion for Collaborative Automated Target Acquisition

OUSD (R&E) MODERNIZATION PRIORITY: Network Command, Control and Communications; Autonomy; Artificial Intelligence/Machine Learning TECHNOLOGY AREA(S): Sensors; Information Systems 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. Please direct questions to the Air Force SBIR/STTR HelpDesk: usaf.team@afsbirsttr.us. OBJECTIVE: AFRL is seeking innovative research to enable near-real-time data and information fusion on limited SWAP platforms to support collaborative automated target acquisition (ATA) in multi-target, multi-agent environments. DESCRIPTION: The Munitions Directorate of the Air Force Research Laboratory is soliciting white papers under this Broad Agency Announcement (BAA) for research, development, and evaluation of technologies/techniques to enable near-real-time collaborative ATA based on data and sensor fusion in complex adversarial environments. As collaborative munitions become more pervasive, warfighters seek to maximize the benefits of swarming and autonomy to include employing near real time identification and tracking of multiple targets during their relatively short flight times (seconds-to-minutes). These operations will be carried out by platforms that have limited SWAP and modest communication capabilities that must be low-latency, using heterogeneous mixtures of sensing modalities in highly complex environments. To combat these challenges future operational concepts will incorporate networked, heterogeneous, AI-enabled, real-time sensing systems on autonomous/semi-autonomous platforms. Such systems will support autonomous targeting in near-real-time (e.g., seconds). It has been recognized that diverse sensors and information types will be required to overcome a combination of obscured targets, multiple targets and confounders, and high-consequence actions. The successful proposal will address how to combine a priori data into a state-based construct that a) optimizes real-time data collection, and b) minimizes real-time communication requirements. PHASE I: Conceptualize, develop, and model an algorithmic solution that provides near real-time collaborative ATA for heterogeneous sensors. PHASE II: Implement, prototype, and demonstrate the near real-time collaborative ATA function. PHASE III DUAL USE APPLICATIONS: Adapt and implement the collaborative ATA function into a selected collaborative munition system. REFERENCES: Kim, Sungho, Woo-Jin Song, and So-Hyun Kim. "Robust ground target detection by SAR and IR sensor fusion using Adaboost-based feature selection." Sensors 16, no. 7 (2016): 1117; Zhou, Y., Sun, X., Zha, Z.J. and Zeng, W., 2019. Context-reinforced semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4046-4055); Volle, K., Rogers, J. and Brink, K., 2016. Decentralized cooperative control methods for the modified weapon target assignment problem. Journal of Guidance, Control, and Dynamics, 39(9), pp.1934-1948. KEYWORDS: sensor fusion; information fusion; data fusion; machine learning; target identification; swarms; swarming; collaborative munitions