Trajectory estimation of a moving object using Kohonen networks

Abstract

A novel approach to estimate the real-time moving trajectory of an object is proposed in this article. The object’s position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Kalman filter and neural networks are utilized. Since the Kalman filter needs to approximate a nonlinear system into a linear model to estimate the states, there always exist errors as well as uncertainties again. To resolve this problem, neural networks are adopted in this approach which have high adaptability with the memory of the input–output relationship. A Kohonen network (self-organized map) is selected to learn the motion trajectory since it is spatially oriented. The superiority of the proposed algorithm is demonstrated through real experiments.