Statistical efficiency of composite position measurements from passive sensors

摘要

Combining line-of-sight (LOS) measurements from passive sensors (e.g., satellite-based IR, ground-based cameras, etc.), assumed to be synchronized, into a single composite Cartesian measurement (full position in 3D) via maximum likelihood (ML) estimation, can circumvent the need for nonlinear filtering. This ML estimate is shown to be statistically efficient, and as such, the covariance matrix obtainable from the Cramer-Rao lower bound provides a consistent measurement noise covariance matrix for use in a target tracking filter