Multiple Model Adaptive Estimator Using First and Second-Order AccelerationModels for Use in a Forward-Looking Infrared Tracker

摘要

The performance of a multiple model adaptive estimator (MMAE) for an enhancedcorrelator/forward-looking-infrared tracker for airborne targets is analyzed in order to improve its performance. Performance evaluation is based on elemental filter selection and MMAE estimation error sizes and trends. The elemental filters are based on either first or second-order acceleration models. Improved filter selection is achieved by using acceleration models that separate the frequency content of acceleration power spectral densities into non-overlapping regions with second-order models versus the more traditional overlapping regions with first-order models. A revised tuning method is presented. The maximum a posteriori (MAP) versus the Bayesian MMAE is investigated. The calculation of the hypothesis probability calculation is altered to see how performance is affected. The impact of the ad hoc selection of a lower bound on the elemental filter probability calculation to prevent filter lockout is evaluated. Parameter space discretization is investigated. Comparable performance is achieved from the MMAEs based on either first or second-order acceleration models. The MAP and Bayesian options give comparable performance. A lower bound of 0.001 gives best results. The traditional probability calculation allows better filter selection by the MMAE for this application.