In the era of big data, it appears imperative that future battle management systems be able to identify, decipher, and prioritize actionable information. This calls for fusing information and synchronizing operations across multiple domains and multiple sensor modalities. Fusing data from a diverse range of sensors across multiple domains is critical for improved situational awareness to enhance warfighters’ effectiveness. The basis for analyzing multiple field radar system data in real time remains a challenging yet promising threshold for military operational intelligence. Multiple domain sensor systems used to gather field intelligence requires gathering different types of information processing at required speeds that fall short of human reaction time and cognition. To press the advancement of field intelligence, the analysis, fusion and optimization of multi-domain systems, sensor data analysis is explored using probabilistic machine learning and supplemented heuristic signal processing to provide a basis for multi-system data integration, analysis and sensor suite selection.
Ferris I. Arnous, Ram M. Narayanan, and Bing C. Li, "Artificial intelligence implementation for multi-domain sensor suite optimization," Proc. SPIE 11408, Radar Sensor Technology XXIV, 1140818 (Presented at SPIE Defense + Commercial Sensing: April 29, 2020; Published: 23 April 2020); https://doi.org/10.1117/12.2567139.
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