Distributed Multispectral Event-Driven Integrated Sensing and Communications

SBIR topic DON27BZ02-NV009 (U.S. Department of Defense – Navy), Phase I and II. Open to U.S. small businesses. Opens: December 2, 2026. Closes: January 6, 2027. The Navy Aegis Program Office is interested in expanding battlespace awareness to support cross-domain operations through the integration of remote passive sensors. Currently, these sensors rely on real-time frame-based processing that generates large volumes of data, requiring significant processing power and energy and creating constraints for resource-limited hardware platforms with finite battery life, processing capabilities, and memory. In addition, transmitting large volumes of data over dynamic communication links can introduce delays and encounter bandwidth limitations that cannot meet time-critical operational needs. The complexity of the battlespace and electromagnetic environment further presents challenges for robust detection, classification, and identification. Advanced remote passive-sensor technologies that enable efficient and distributed sensing and processing will support multidomain operations by providing timely and resilient battlespace awareness. Successful technologies will be transitioned to the Aegis Combat System (ACS) to enhance distributed sensing, accelerate decision-making, and enable cross-domain operations. The Navy is seeking to develop Multispectral Integrated Neuromorphic Sensing and Communications (MINSC) technology to support endurant remote sensing capability. MINSC is an event-driven sensor featuring low-probability-of-intercept (LPI) data transmission that can timely identify a wide range of objects and transmit the information to remote platforms to integrate into the existing decision-making process. Neuromorphic computing can provide energy efficient, parallel processing, and low-latency framework for passive sensing, edge computing, and remote inference. Neuromorphic sensors deliver high temporal resolution, asynchronous, energy and bandwidth efficient data…