Development of an Adaptive Kalman Target Tracking Filter and Predictor for Fire Control Applications.

摘要

This report describes the development of an adaptive Kalman filter for target tracking and prediction that was subsequently implemented in the digital MARK 68 Gunfire Control System (GFCS) as part of the Gunnery Improvement Program. The discrete Kalman filter is introduced along with a brief discussion of its selection for this application. The general problem of target modeling was presented with emphasis on conventional polynomial models and their convergence properties. A stochastic target model, a first order Markov process in acceleration, was introduced, and the advantages over the polynomial were models explored. A dual bandwidth adaptation algorithm with associated maneuver detection logic was developed and favorably compared with more conventional adaptation methods. A Kalman filter to handle serially correlated observation error (without state vector augmentation) was found, restructured to improve the computational efficiency and exercised to determine parametric sensitivity to correlation effects. Prefiltering, or data compression techniques, were studied and found to significantly reduce required computation with negligible degradation in performance. Square root covariance propagation (in single precision) was found to be considerably more efficient (by a factor of 4.5) than double precision covariance for the particular filter model and computer for this application. The three-dimensional filtering problem was approached by first developing the optimal nonlinear filter as a standard and then evaluating on a relative basis several suboptimal linearized versions.