Abstract
This paper examines the use of various estimation filters on the highly non-linear problem of tracking a ballistic missile during boost phase from a moving airborne platform. The aircraft receives passive bearing data from an IR sensor and range data from a laser rangefinder. The aircraft is assumed to have a laser weapon system that requires highly accurate bearing information in order to keep the laser on target from a distance of 100-200 km. The tracking problem is made more difficult due to the changing acceleration of the missile, especially during stage drop-off and ignition. The Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), 'bootstrap' Particle Filter (PF), and the Gaussian Sum Particle Filter (GSPF) are explored using different values for sensor accuracy in bearing and range, and various degrees of uncertainty of the target and platform dynamic. Scenarios were created using Satellite Toolkit<sup>©</sup> for trajectories from a Southeast Asia launch with associated sensor observations. MATLAB<sup>©</sup> code modified from the ReBEL Toolkit<sup>©</sup> was used to run the EKF, UKF, PF, and GSPF sensor track filters. Mean Square Error results are given for tracking during the period when the target is in view of the radar and IR sensors. This paper provides insight into the accuracy requirements of the sensors and the suitability of the given estimators.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.