Publication

Abstract : The paper develops a basic idea pertaining to Kalman filters. The interest in Kalman filter is motivated by the wide variety of applications it provide. The Kalman filter is an iterative filter which considers a series of measurements (that contains statistical noise) over time, and then estimates the unknown variable. The primary concern is to design a block in GNU Radio for the operation of Kalman filtering. In this work, a block has been created and we explored the filtering algorithm using three different key in mechanisms - 1)a continuous signal dataset, 2)an open flight tracker dataset, 3)a generated dataset. The first mechanism include providing a continuous sinusoidal signal source to the filter block. Then, an open aircraft dataset used in tracking the position of flight was considered and had been fed to experiment the Kalman filter block. Further, a dataset generated by considering the practical possibilities of a giant moving object was input to the block. The result of all the three mechanisms confirm that there is an effective noise reduction in position tracking, on using Kalman filtering algorithm.