An application of a Kalman Filter Fixed Interval Smoothing Algorithm to underwater target tracking

Abstract

A Fortran program was developed to implement a Kalman Filter and Fixed Interval Smoothing Algorithm to optimally data tracks generated by the short base-line tracking ranges at the Naval Torpedo Station, Keyport, Washington. The program is designed to run on a personal computer and requires as input a data file consisting of X, Y, and Z position coordinates in sequential order. Data files containing the filtered and smoothed estimates are generated by the program. This algorithm uses a second order linear model to predict a typical target's dynamics. The program listings are includes as appendices. Several runs of the program were performed using actual range data as inputs. Results indicate that the program effectively reduces random noise, thus providing very smooth target tracks which closely follow the raw data. Tracks containing data generated in an overlap region where one array hands off the target to the next array are highlighted. The effects of varying the magnitude of the excitation matrix Q(k) are also explored. This program is seen as a valuable post-data analysis tool for the current tracking range data. In addition, it can easily be modified to provide improved real time, on line tracking using the Kalman Filter portion of the algorithm alone