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MOTION PARAMETERS ESTIMATION OF A SMALL OBJECT BASED ON SWITCHING OF MOTION MODELS

V. A. Belokurov, PhD, assistant professor, RSREU, Ryazan, This email address is being protected from spambots. You need JavaScript enabled to view it.

Detection of small objects with low signal-to-noise ratio, as well as the estimation of the parameters of its motion is a complex and urgent problem which is seen in radio and infrared range and direction detection, especially in conditions of inaccurately given model of the object. The aim of work is the synthesis of an algorithm of joint detection and estimation of the parameters of a small object motion. To solve this problem we propose to enter several models of the object into partial Gaussian filtering algorithm. In this paper, we consider two models of motion: linear motion and coordinate turn. Changing patterns of movement takes place on the basis of transition probabilities of simply connected Markov sequence model of the object. A comparison of the proposed algorithm and the well-known one on the basis of numerical calculation of a posteriori distribution density of state object vector is made. Numerical simulations show that the synthesized algorithm improves the accuracy of motion estimation parameters (up to 3,5 times) in the case of object maneuvering.

Key words: Gaussian particle filter, tracking before detection, low signal-to-noise ratio, motion models switching, first-order Markov sequence.

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