Abstract:
This dissertation provides a new methodology of using star observations and advanced nonlinear estimation algorithms to improve the ability of a space based Infrared tracking system to track cold body targets in space. Typically, the tracking system consists of two satellites flying in a lead-follower formation tracking a ballistic or space target. Each satellite is equipped with a narrow-view IR sensor that provides azimuth and elevation measurements to the target. The tracking problem is made more difficult due to a constant, non-varying or slowly varying bias error present in each sensor‟s line of sight measurements. The conventional sensor calibration process occurs prior to the start of the tracking process and does not account for subsequent changes in the sensor bias. This dissertation develops a technique to estimate the sensor bias from celestial observations while simultaneously tracking the target. As stars are detected during the target tracking process the instantaneous sensor pointing error can be calculated as the difference between a measurement of the celestial observation and the known position of the star.
The system then utilizes a separate bias filter to estimate the bias value based on these measurements and correct the target line of sight measurements. The study develops and compares the ability of three advanced nonlinear state estimators: A Linearized Kalman Filter; an Extended Kalman Filter; and an Unscented Kalman Filter, to update the state vector. The bias correction-state estimation algorithm is validated using a number of scenarios that were created using The Satellite Toolkit©. The variance of the target position error resulting from the nonlinear estimation filters is compared to the posterior Cramer-Rao lower bound and a filter consistency check. The results of this research provide a potential solution to sensor calibration while simultaneously tracking a space borne target with a space based sensor system.
