Performance of Predictors with Non-Gaussian Inputs

Abstract

The trajectory of a maneuvering aircraft can be described by piecewise-constant-coefficient second-order polynomials which constitute a non-Gaussian process at the input of a linear fire control predictor. Consequently, the ?on- target? probability which is determined by the predictor output cannot be analytically evaluated. Using this a priori assumption for this type of target trajectory, the structure of a finite-time polynomial filter is determined. By using the ?go-no-go? characteristic of the loss function, the smoothing time of the predictor can be optimized to yield the highest possible ?on-target? probability. The results obtained constitute lower bounds whose validity is verified by digital simulation of the system. This shows that, on the average, they are 15 percent below the exact values. The simulation program is also used in a comparative study, with a conventional predictor and a Wiener-type predictor. The results demonstrate that the finite time polynomial predictor is markedly superior to both systems for a wide range of parameters and their combinations.