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Intercepting reentry vehicles is difficult because these move nearly at hypersonic speeds that traditional interceptors cannot match. Counterparallel guidance law was developed for defending a high speed target that guides the interceptor to intercept the target at a 180° aspect angle. When applying the counterparallel guidance law, it is best to predict the impact point before launch. Estimation and prediction of a reentry vehicle path are the first steps in establishing the impact point prediction algorithm. Model validation is a major challenge within the overall trajectory estimation problem. The adaptive Kalman filter, consising of an extended Kalman filter and a recursive input estimator, accurately estimates reentry vehicle trajectory by means of an input estimator which processes the model validation problem. This investigation presents an algorithm of impact point prediction for a reentry vehicle and an interceptor at an optimal intercept altitude based on the adaptive Kalman filter. Numerical simulation using a set of data, generated from a complicated model, verifies the accuracy of the proposed algorithm. The algorithm also performs exceptionally well using a set of flight test data. The presented algorithm is effective in solving the intercept problems.

When a gun fires, a large amount of heat flux is triggered by the propellant gas acting on the gun barrel inner wall, leading to the rise of temperature, which will cause serious destruction. In this paper, an inverse method based on the input-estimation method including the finite inverse heat conduction problem (IHCP) element scheme to inverse estimate the unknown heat flux on the 2-D gun barrel has been presented. The use of the online accuracy to inversely estimate the unknown heat flux on the chamber has been made using 7.62 mm gun barrel outer wall temperature measurement data. Using simulation uniform and non-uniform heat flux q(z,t) cases involves a gun barrel inner wall that varies with time t and the axial z-location with convection situation in the outer surface. Computational results show that the proposed method exhibits a good estimation performance and highly facilitates practical implementation.