How to fuse sensor data for accurate target tracking?
Collaborative State Fusion in Partially Known Multi-agent Environments
October 22, 2024
https://arxiv.org/pdf/2410.15137- The research tackles the problem of combining information from multiple AI agents (like sensors) to accurately track moving targets, even when the agents have limited and potentially faulty data.
- It proposes LoF, a two-stage framework where agents first estimate target states individually, then a central process combines these estimates robustly, even with errors or inconsistencies. LoF learns to weigh agents' estimates based on their past reliability, making it adaptable to changing conditions. This is relevant to LLM-based systems where agents could be LLMs processing and interpreting complex data, with LoF ensuring reliable combined conclusions.