System Identification of FIR Filters
by Sudheesh Kannur Vasudeva Rao * 
, Kiran , Naveen Kumar , Mahadevaswamy
Department of ECE, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India-570002
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 4, Page # 74-80, 2022; DOI: 10.55708/js0104010
Keywords: System Identification, Finite Impulse Response Filter, Adaptive Filter, Least Mean Square Algorithm, NLMS Algorithm, RLS Algorithm
Received: 28 February 2022, Revised: 23 March 2022, Accepted: 28 March 2022, Published Online: 12 April 2022
APA Style
Rao, S. K. V., Kiran, K., Kumar, N., & Mahadevaswamy, M. (2022, April). System Identification of FIR Filters. Journal of Engineering Research and Sciences, 1(4), 74–80. https://doi.org/10.55708/js0104010
Chicago/Turabian Style
Rao, Sudheesh Kannur Vasudeva, Kiran Kiran, Naveen Kumar, and Mahadevaswamy Mahadevaswamy. “System Identification of FIR Filters.” Journal of Engineering Research and Sciences 1, no. 4 (April 2022): 74–80. https://doi.org/10.55708/js0104010.
IEEE Style
S. K. V. Rao, K. Kiran, N. Kumar, and M. Mahadevaswamy, “System Identification of FIR Filters,” Journal of Engineering Research and Sciences, vol. 1, no. 4, pp. 74–80, Apr. 2022, doi: 10.55708/js0104010.
Identification of Finite Impulse Response (FIR) filters refer to finding out the coefficients also known as the weights of its transfer function. Adaptive filtering using Least Mean Square (LMS) Algorithm is used to find the estimated weights of the transfer function, using ATMEGA16 processor. This method can be used to find the coefficients of complex resistive circuits. This is done by constantly comparing the FIR system with Adaptive filter until the difference signal is zero, both the systems are fed with same input signals.
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