A system approach to real world multi-sensor data fusion
Fred E. Daum · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
Theoretically, fusion of measurements is optimal, and fusion of tracks is suboptimal. However, in the real world it frequently turns out that track fusion is superior to measurement fusion. The purpose of this paper is to explain the main reasons for this and to describe a measurement fusion algorithm that is robust in the real world. The two major reasons that measurement fusion fails to perform as well as expected in the real world are: (1) limited sensor resolution, and (2) residual sensor bias errors. Designing robust multi-sensor data fusion algorithms that work well in the real world requires understanding the physics of both sensor resolution and sensor bias errors.