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.

Research highlights
► A probabilistic framework, using a Gaussian mixture model, for fusing location estimates, is presented.
► The location estimates may be biased and inconsistent.
► The adaptive method accommodates bias when it is present, without significant degradation when it is absent.
► It is intended for use when it is not feasible to model and remove the sensor bias.
► A passive geolocation application involving an airborne Electronic Support (ES) system is considered.