Publication Type:

Conference Paper


2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), IEEE, Nagercoil, India (2016)



adaptive filter, Adaptive filters, Adaptive systems, air-borne vehicle, Estimation, Kalman filter, Kalman filters, mathematical model, Noise measurement, tracking, Wiener filters


This paper focuses on linear Kalman Filter and its application in 2-D tracking of airborne vehicles. Kalman filter is a powerful computation device which uses recursive computation to attain solution of discrete linear filtering. Being an adaptive filter, Kalman filter analysis the relation between its estimated value and measured value, through a feedback loop and tries to attain the result after minimising the noises in the measured value. A system based on control systems, its estimation required can be of the past, presentor the future. In this paper, application of Kalman filter for tracking has been validated with tracking of an air-borne vehicle with constant velocity and constant deceleration. The model was validated with the SNR v/s NMSE graph. Kalman Filter is provided with the (x, y) coordinates and the velocity in each coordinate based on which the next set of coordinates are estimated by the Kalman Filter. Based on the accuracy of the modelling, Kalman Filter might require several estimations to adapt and give more precise estimations. The code has been written with iterations within estimations. Further, the identification of state equations and their relation to this application has been studied.

Cite this Research Publication

N. R. Nair, Sudheesh, P., and Jayakumar, M., “2-D airborne vehicle tracking using Kalman filter”, in 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, India, 2016.