Bias modeling and estimation for GMTI applications

This paper describes an approach to sensor bias modeling and estimation for ground target tracking applications using multiple airborne Ground Moving Target Indicator (GMTI) radar sensors. This approach was developed as part of the Precision Firecontrol Tracking (PFCT) segment of the DARPA Affordable Moving Surface Target Engagement (AMSTE) program. For airborne sensors, slowly varying platform location, heading and velocity errors lead to time-dependent measurement biases. Track accuracy can be improved by using a Kalman filter to estimate and correct the biases in real time, based on fixed reference points. The reference point location can be known a priori or estimated online as part of the bias correction algorithm. When the reference locations are known a prior, bias effects can be nearly completely eliminated. When the reference point is estimated online, significant performance improvement is obtained relative to uncorrected measurements.