Algorithms for distributed chemical sensor fusion

The fusion of Chemical, Biological, Radiological, and Nuclear (CBRN) sensor readings from both point and stand-off sensors requires a common space in which to perform estimation. In this paper we suggest a common representational space that allows us to properly assimilate measurements from a variety of different sources while still maintaining the ability to correctly model the structure of CBRN clouds. We design this space with sparse measurement data in mind in such a way that we can estimate not only the location of the cloud but also our uncertainty in that estimate. We contend that a treatment of the uncertainty of an estimate is essential in order to derive actionable information from any sensor system; especially for systems designed to operate with minimal sensor data. A companion paper1 further extends and evaluates the uncertainty management introduced here for assimilating sensor measurements into a common representational space.