Improved Track Consistency for Improved Multiple Sensor Tracking (MDA04-121)

The objective of this Phase I project is to demonstrate the feasibility for new algorithms to substantially improve track consistency in sensor fusion. Track consistency is the property that the computed error covariance matrix of a track realistically represents the covariance of the actual errors of the estimated target state. The computed error covariance matrix is used in data (and track) association processing and to indicate the accuracy of the target state estimate. Consequently, degraded track consistency causes misassociations (correlation errors) that will substantial degrade track performance and the performance of subsequent processing, such as for discrimination, sensor resource management, and battle management. If an inconsistent track covariance is also used in the subsequent processing then that process will exhibit additional degradation. The new algorithms will be subjected to Monte Carlo runs to show Proof of Principle. These processing methods have been designed to correct for the common causes of degraded track consistency in fusion tracks, such as misassociations (miscorrelations), model errors, and residual sensor biases. Results from high fidelity simulations have shown that degraded track consistency is a major problem even with more advanced fusion processing than is in current operational systems or field tested.