Combat System Filter Engineering

Abstract

To develop a new combat system, the designer has to determine the optimal filter type, filter application point, and dynamic model assumption. The definition of an optimal filter will change depending on the filter’s purpose and can change drastically depending on its application within the plan–detect–control–engage sequence. Ideally, the designer could apply a single computationally efficient filter algorithm that adapts in real time to threat maneuver, system bias, and measurement noise level while maintaining an accurate estimate with a high level of confidence. In practice, however, several different filters (often of different types) are applied to a single combat system in separate parts of the plan–detect–control–engage sequence to ensure the best results for problems that include track consistency, association, filter errors, and correlation. There are several key factors in developing a robust filter with the flexibility for the technology upgrades that are required to keep up with threat evolution. This article describes a design methodology to provide robustness in the face of dynamic threat behavior, lack of a priori knowledge, and threat evolution.