Innovation-Based Fusion of Multiple Satellite Positioning Systems for Minimizing Uncertainty

Redundant outputs from multiple positioning systems are not typically fused together on moving vehicles, resulting in unrealized opportunities in minimizing any combined position error and uncertainty. Satellite navigation is commonly used in timing and positioning applications, and combined with complementary inertial information to generate a more complete solution. However, the use of multiple Global Positioning System (GPS) outputs has not been thoroughly explored within a decentralized fusion context for reducing the uncertainty and position error. To prospect any such improvements, a federated architecture using the position outputs of multiple GPS with an inertial measurement unit is considered for a ground vehicle. The innovations are derived from extended Kalman filters and used in weighting the combination of outputs, under conditions of independence, estimated correlations, and unknown cross covariance. The methods of averaging, maximum likelihood estimation, covariance trace optimization, and covariance intersection are compared. As the number of systems grows, simulations show that the root mean squared (horizontal) position error decreases. The methods that account for nonzero correlations provide the most improved fused mean position error. As redundant position data become more ubiquitous, it can be used to improve the fused estimate over any single system alone, while further increasing overall robustness.