Context Aware Processing Optimization

Abstract and Benefits SummaryAbstractThe evolution of radio frequency (RF) systems has been integral to military communications, intelligence, and operations since their inception in the early 20th century. As RF technology has advanced, so has the volume and complexity of the data these systems generate. Modern military operations must contend with voluminous RF data streams, presenting significant challenges in identifying which signals warrant immediate action or storage. This summary explores the historical context of RF use in the military, outlines the current challenges, and presents a proposal for intelligent signal processing to enhance decision-making and operational efficiency.RF technology has been a cornerstone of military communication since World War I, providing crucial links for coordination and intelligence. In World War II, RF systems became more sophisticated, enabling radar and secure communications. The Cold War era saw further advancements with the development of satellite communications and electronic warfare capabilities. Despite these technological leaps, the fundamental problem of managing and interpreting large volumes of RF data has persisted. During a recent military exercise, a task force was overwhelmed by the sheer amount of RF signals intercepted, leading to delays and missed opportunities in critical decision-making.Today’s military RF systems generate data at unprecedented rates due to the proliferation of advanced sensors, communication devices, and electronic warfare systems. This deluge of data presents two primary challenges: Signal Prioritization: Identifying which RF signals are critical and require immediate action versus those that can be deprioritized or ignored.Data Storage: Efficiently storing relevant signals for future analysis without overwhelming storage capabilities.These challenges are exacerbated by limited processing resources and the need for rapid, accurate decision-making in dynamic operational environments.Context Aware Processing Optimization (CAPO) addresses these challenges through an intelligent allocation of processing assets and a decision agent designed to maximize the utility of RF streams. By leveraging advanced algorithms and machine learning, our system can dynamically prioritize and manage RF data, ensuring that the most critical signals are processed and acted upon promptly.Our solution leverages advanced computing, integrated sensing, and trusted AI/ML and autonomy to provide a self-learning, context-aware pre-processing system.  MTRI will leverage their Multi Armed Bandit (MAB) approach to determine the most relevant signals of interest to be tasked on the hardware.  DornerWorks will implement the AI/ML algorithm on the most appropriate hardware based on evaluation to maximize the throughput and speed of the system.