Bias estimation for optical sensor measurements with targets of opportunity

This paper provides a solution for bias estimation of multiple passive sensors using common targets of opportunity. The measurements provided by these sensors are assumed time-coincident (synchronous) and perfectly associated. Since these sensors provide only line of sight (LOS) measurements, the formation of a single composite Cartesian measurement obtained from fusing the LOS measurements from different sensors is needed to avoid the need for nonlinear filtering. The evaluation of the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases, combined with simulations, shows that this method is statistically efficient, even for small sample sizes.