Second-order acceleration models for a multiple model adaptive estimator in a FLIR tracker

The performance of a multiple-model adaptive estimator (MMAE) for an enhanced correlator/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 nonoverlapping 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 estimator is compared with the Bayesian MMAE. The calculation of the hypothesis probability 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