Fusion of possibly biased location estimates using Gaussian mixture models

A probabilistic framework for fusing location estimates, which may be biased and inconsistent, is presented. The proposed method, involving Gaussian mixture models (GMMs), utilizes prior information regarding the sensor bias, firstly, to reduce errors in the fused location estimate, and secondly, to produce a fused covariance matrix that better reflects the expected location error. Simulations are used to evaluate performance, relative to other techniques, such as the covariance union (CU) method. A passive geolocation application involving an airborne electronic support (ES) system is considered.