Estimation filters for missile tracking with airborne laser

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^© for trajectories from a Southeast Asia launch with associated sensor observations. MATLAB^© code modified from the ReBEL Toolkit^© 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.