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

continue on reverse if necessary and identify by block number) A Fortran program was developed to implement a Kalman Filter and Fixed Interval Smoothing Algorithm to optimally nooth data tracks generated by the short base-line tracking ranges at the Naval Torpedo Station. Keyport. Washington. he program is designed to run on a personal computer and requires as input a data file consisting of X. V. and Z position >ordinates in sequential order. Data files containing the filtered and smoothed estimates are generated by the program. This gorithm uses a second order linear model to predict a typical target's dynamics. The program listings are included as ap.ndices. Several runs of the program were performed using actual range data as inputs. Results indicate that the program effectively duces random noise, thus providing very smooth target tracks which closely follow the raw data. Tracks containing data merated in an overlap region where one array hands off the target to the next array are highlighted. The effects of varying .e 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 modified to provide improved real time, on line tracking using the Kalman Filter portion of the algorithm alone. J Distribution Availability of Abstract . --classified unlimited D same as report DTIC users 21 Abstract Security Classification Unclassified a Name of Responsible Individual arold A. Titus 22b Telephone < include Area code) 22c Office Symbol (40S) 646-2560 62TS ::> FORM 1473,84 MAR S3 APR edition may be used until exhausted All other editions are obsolete security classification of this page Unclassified Approved Tor public release; distribution is unlimited. An Application of a Kalman Filter Fixed Interval Smoothing Algorithm to Underwater Target Tracking by Richard B. Nicklas Lieutenant, United States Navy B.S.E.E., United States Naval Academy, 1982 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN ELECTRICAL ENGINEERING from the NAVAL POSTGRADUATE SCHOOL March 1989 Gordon E. Schacher, Dean of Science and Engineering ABSTRACT A Fortran program was developed to implement a Kalman Filter and Fixed Interval Smoothing Algorithm to optimally smooth 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 included 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 verysmooth 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.A Fortran program was developed to implement a Kalman Filter and Fixed Interval Smoothing Algorithm to optimally smooth 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 included 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 verysmooth 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.