Open AccessMachine Learning Based Track Classification and Estimation using Kalman Filter
Author(s) -
B. Eswara Reddy,
J. Valarmathi
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a2616.059120
Subject(s) - kalman filter , clutter , computer science , artificial intelligence , minimum mean square error , pattern recognition (psychology) , noise (video) , mean squared error , filter (signal processing) , track (disk drive) , statistics , mathematics , computer vision , radar , telecommunications , estimator , image (mathematics) , operating system
Classification of target from a mixture of multiple target information is quite challenging. In This paper we have used supervised Machine learning algorithm namely Linear Regression to classify the received data which is a mixture of target-return with the noise and clutter. Target state is estimated from the classified data using Kalman filter. Linear Kalman filter with constant velocity model is used in this paper. Minimum Mean Square Error (MMSE) analysis is used to measure the performance of the estimated track at various Signal to Noise Ratio (SNR) levels. The results state that the error is high for Low SNR, for High SNR the error is Low.