Harish Parthasarathy

Work place: Department of Electronics & Communication Engineering, Netaji Subash Institute of Technology, New-Delhi, India

E-mail: harishsignal@yahoo.com

Website:

Research Interests: Engineering, Computational Engineering, Computational Science and Engineering

Biography

Prof. Harish Parthasaraty received B.Tech in 1990 (from Indian Institute of Technology, Kanpur, India) and Ph. D. (from India Institute of Technology, Delhi, India) in 1994, both in Electrical Engineering. Presently, he is working as Professor in the division of Electronics & Communication Engineering at Netaji Subash Institute of Technology, New-Delhi. His teaching and research interests are in the areas of circuits and systems, signal processing, stochastic nonlinear filters, electromagnetics and group representations and he has published 30 papers in various international journals of repute.

Author Articles
Two-Dimensional Parameters Estimation

By Shiv Gehlot Harish Parthasarathy Ravendra Singh

DOI: https://doi.org/10.5815/ijigsp.2016.09.01, Pub. Date: 8 Sep. 2016

A parametric approach algorithm based on maximum likelihood estimation (MLE) method is proposed which can be exploited for high-resolution parameter estimation in the domain of signal processing applications. The array signal model turns out to be a superposition of two-dimensional sinusoids with the first component of each frequency doublet corresponding to the direction of the target and second component to the velocity. Numerical simulations are presented to illustrate the validity of the proposed algorithm and its various aspects. Also, the presented algorithm is compared with a subspace based technique, multiple signal classification (MUSIC) to highlight the key differences in performance under different circumstances. It is observed that the developed algorithm has satisfactory performance and is able to determine the direction of arrival (DOA) as well as the velocity of multiple moving targets and at the same time it performs better than MUSIC under correlated noise. 

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