- R. L. Moos
- O. Wang, "An Adaptive Estimator with Learning for…
This report describes an adaptive state estimator that can significantly improve the passive range and depth determination of a randomly maneuvering target. The target in this study is a submarine, which, while being tracked, performs large-magnitude depth changes at times unknown to the tracking submarine. Present passive tracking techniques usually utilize a Kaiman filter to process the azimuth and/or elevation observations. A Kaiman filter will theoretically give the "best" estimates of target range, depth, and velocity when the system and measurement errors can be modeled as Gaussian processes. Themain difficulty in using a Kaiman filter in passive tracking applications is that large bias errors invariably develop as the target makes large alterations in velocity or depth. A technique for including a feedbacktype learning processor in conjunction with the Kaiman filter has been found to greatly reduce bias errors produced by the maneuvering target. This error elimination is accomplished with a negligible increase in computer storage and a small increase in computation time. The method is general in nature and can be applied to other types of tracking situations in which a target randomly undergoes large-magnitude changes in motion.