Estimating Characteristics of a Maneuvering Reentry Vehicle Observed by Multiple Sensors

Post flight analysis of ballistic missile reentry vehicles is an area of focus for the U.S. Government, especially for those involved in ballistic missile defense. Typically, this analysis incorporates either a model-driven least squares filter or a data-following Kalman filter. The research performed here developed a filter that attempts to integrate the strengths of both filters. A least squares filter operates on observation data collected during exoatmospheric free flight and a Kalman filter is used to analyze data collected lower in the atmosphere, where potential maneuvers could be performed. Additionally, the filter was written to incorporate data from multiple sensors. Using this hybrid filter, different scenarios are investigated to determine the potential benefits of adding additional collectors, increasing the data rate of collecting sensors, and investigating the effects of different collector geometry on the accuracy of results. Results show that the filter successfully transitions from the least squares to Kalman filter, using the final values of the free flight propagation for the Kalman filter’s initial state. Using this filter to investigate different collection scenarios, it was determined that the best results are achieved when multiple collectors are used, the data collection rate of the collectors is increased, and collectors are positioned perpendicular to the reentry vehicle heading