Adaptive estimation of an object motion parameters based on the hybrid stochastic model

Abstract

This paper examines the problem of estimating parameters of the motion of an object moving along a complex trajectory in the presence of incomplete noisy measurements. This problem is of current interest because of the importance of its practical applications. Examples of such applications are the tracking problems of moving technical devices in robotics, navigation, ground vehicles. We propose a solution to the problem of adaptive estimation of the motion parameters of an object based on the hybrid stochastic model, which is a set of discrete linear stochastic models corresponding to various pieces of the object’s trajectory. The linearity of the models allows us to apply the optimal Kalman filtering to calculate the estimates of the motion parameters of an object. To identify the current motion mode of an object at a known time moments, new algorithm based on the use of the Kalman filters bank and sequential ratio probability test is developed. Numerical experiments confirm the efficiency of the obtained results.