Missile Control Parameters Estimation That Uses Robust Adaptive Kalman Filter Algorithm

摘要

The standard Kalman Filter (KF) algorithm can't estimate the control parameters accurately when there are errors in the model of missile control system. Thus, the state-space equations and observation equations of the testing parameter were established, based on Constant Acceleration (CA) model. Then the principle of standard KF and the impact of test errors to the filter estimate results were analyzed, and the method of dynamically adjusting the weight of prediction information in the filter estimate result was introduced, then the Robust-adaptive Kalman Filter (RAKF) principle and the recursion formula were presented. Finally the algorithm and system model were verified using the simulated data. The calculation results comparing with standard KF show that the designed RAKF has better estimate precision, when the model error is given.