Classification and Engineering Analysis of Solid Propellant Missile Telemetry Data

Abstract

This paper describes classification methods of missiles using solid rocket motors. Two classification scenarios are described: one shows how to classify shortly after launch, and another shows how to use the “max” parameters of the trajectory for classification. The first scenario will show that, within the first 30 s of telemetry data, the missile class can be quickly and accurately determined. To extend this scenario, we will then determine how much data are necessary to classify, so classification will then be attempted in the first 10 and 20 s. The importance of the telemetry data can then be quantified by a direct calculation of the Shapley values. To analyze robustness, missing data are simulated and imputed for classification. The second scenario utilizes the max parameters of the telemetry, such as apogee, to classify the missile. This scenario is more akin to analyzing trajectories that have already been completed. Various classifications using different inputs for classification are shown. Both scenarios will utilize Fisher’s discriminant analysis and neural networks to compare performance, and the main goal is to classify missiles. Using discriminant analysis and neural networks provides high accuracy.