Correlated error analysis for the non-linear optimization AoA geolocation algorithm

Previously the Gauss Newton method has been used to estimate the geo-location of an object from angle of arrival (AoA) measurements. This method has assumed, however, that all measurements were independent and identically distributed. Real sensor data, however, often has temporal correlations between measurements. If a detailed understanding of the measurement correlation exists, this correlation can be explicitly modeled and jointly estimated with the geo-location. Obtaining a detailed and accurate model of measurement error correlation, however, is often infeasible for a system where the unit producing measurements may be a black box. To overcome this unknown correlation between measurements, we propose a modified Gauss-Newton optimization algorithm based on prior Covariance Intersection work. A discussion on the efficacy of this modified technique, in terms of both geo-location accuracy and accurate prediction of geo-location uncertainty, concludes the paper.