Practical Approach for Processing and Fusion of Multimodal Data for Reconnaissance

Processing and fusion of multimodal reconnaissance data is critical in the context of intelligence gathering and decision support. We examine the implementation of a suitable architecture and demonstrate the processing and data fusion by exploiting AI-based transcription, translation and Large Language Models information extraction components using audio and text data. We likewise highlight the potential benefits and challenges of integrating them in intelligence workflows. A possible categorisation of components for data processing and fusion according to their practical role is provided.