Compensation of randomly delayed and lost measurements in line of sight guidance law by adaptive Kalman filter

Measurement data of guidance sensors are commonly lost and delayed in ground to air missile systems. These phenomena affect the missile efficiency. Kalman filter is used to estimate the variables needed in implementation of guidance law. But the performance of Kalman filters is dependent on the knowing exact model of the system. In practical problems, the exact parameters of the systems model, especially the one of delay and loss is not known. In this study, adaptive Kalman filter is employed to compensate the uncertainty in the stochastic model of delay and loss which is employed in a line of sight guidance algorithm of a defensive missile. A set of recursive difference equations are used to obtain the adaptive filter gains. The problem is formulated in presence of delayed and missing measurements, then the adaptive filter structure and correction factor are presented. Simulation results are presented to verify the improved performance of the approach.