G.А. Volkov - Senior Engineer, JSC «MKB «Fakel», Moscow. E-mail: gennadiy.a.volkov@yandex.ru D.S. Ivanov - Senior Engineer, JSC «MKB «Fakel», Moscow. E-mail: ivanov_dim@bk.ru

We analyzed the problem of processing homing missile-s seeker measurements during highly maneuvering target intercep. The aim of processing is to get optimal estimates of relative motion parameters based on the measurements from target seeker. These estimates are necessary to generate control commands. The most widespread estimation algorithm, used in the modern information processing systems of homing interceptor missiles, is the discrete Kalman filter. The crucial question during designing the discrete Kalman filter for homing interceptor missile is the question of selection the target maneuver statistical model. Usually a priori information about target motion is considerably limited or completely absent. This fact complicates predicting the target-s behavior and can lead to a divergence of the filter-s estimates. The possible way to solve this problem is to develop an adaptive Kalman filter, correcting target maneuver model depending on current conditions. In this paper considered two methods to develop the adaptive discrete Kalman filter performing in the control loop of homing interceptor missile pursuiting highly maneuvering target. The first approach uses invariable filter-s structure irrespective of the target-s behavior. Adaptation consists in the process noise cova-riance matrix adjustment on basis of comparison measuring residuals with given values. The second approach is based on the Bayesian rule of statistical hypothesis test. It is supposed there are several Kalman filter variants working concurrent (at the same time). We choose one of the Kalman filters - output estimates to generate control commands for the homing missile on basis of the Bayesian rule to make a decision. As a result of mathematical simulation the second approach to develop an adaptive Kalman filter algorithm is acknowledged as the most effective.

 

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