Considered covariance approach to assessing the impact of collector orbital and data measurement biases on geolocation performance

A Gauss–Newton estimator with considered biases is developed and applied to the problem of geolocating a mobile communications signal with an overhead network of receivers. Monte Carlo (MC) simulations are used to validate the model implementation, and to examine practical issues—incorporating navigation errors on the collectors and other biases into the covariance data, analyzing measurement data, and validating algorithm performance. The inclusion of biases dramatically improves containment of the n-dimensional geolocation solution, position and velocity, and provides a mechanism to identify, model, and estimate the impact of physical biases on the time and frequency data. Approaches to improving the convergence of the algorithm, estimating measurement noise, identifying biases on the measurements and collector models, and reporting geolocation data to consumers are described.