Machine Learning Based Track Classification and Estimation using Kalman Filter
B.Sai Tejeswar Reddy1, J.Valarmathi2
1B.SaiTejeswar Reddy*, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
2Valarmathi.J, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 21, 2020. | Manuscript published on May 30, 2020. | PP: 1700-1704 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2616059120/2020©BEIESP | DOI: 10.35940/ijrte.A2616.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Kalman Filter, Linear Regression, Target Classification.
Scope of the Article: Machine Learning