Robust Target State Estimation Using Adaptive Maneuver Models

Improvements in homing guidance laws and high performance autopilots are expected to enable the interception of fast, maneuvering, stealthy targets. However, the full potential of these systems can only be realized if the target states can be accurately estimated. Traditional methods for target state estimation using Kalman filters based on white-noise driven models may not be able to deliver the accuracies required to achieve hit-to-kill capabilities. This proposal advances a target state estimator incorporating an adaptive maneuver logic system. Using the seeker measurements, and a transformed model of the target, the proposed estimator will determine the target maneuver strategies on-line while generating accurate state estimates. Phase I research will establish the feasibility of the proposed target state estimator, and compare its performance with a conventional Kalman filter-based target state estimator. This evaluation will be carried out using a six degrees-of-freedom missile simulation incorporating a high performance autopilot and an advanced differential game-based homing guidance law. Phase II work will focus on developing a target state estimator for the Navy Standard missile, evaluation in several different scenarios of interest to the Navy and on developing code for real-time, on-board implementation of the target state estimator.