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An AR Model Based Robust DOA Estimation

by K.Radhakrishnan, A.Unnikrishnan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 2 - Number 1
Year of Publication: 2010
Authors: K.Radhakrishnan, A.Unnikrishnan
10.5120/606-856

K.Radhakrishnan, A.Unnikrishnan . An AR Model Based Robust DOA Estimation. International Journal of Computer Applications. 2, 1 ( May 2010), 101-104. DOI=10.5120/606-856

@article{ 10.5120/606-856,
author = { K.Radhakrishnan, A.Unnikrishnan },
title = { An AR Model Based Robust DOA Estimation },
journal = { International Journal of Computer Applications },
issue_date = { May 2010 },
volume = { 2 },
number = { 1 },
month = { May },
year = { 2010 },
issn = { 0975-8887 },
pages = { 101-104 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume2/number1/606-856/ },
doi = { 10.5120/606-856 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:49:30.524650+05:30
%A K.Radhakrishnan
%A A.Unnikrishnan
%T An AR Model Based Robust DOA Estimation
%J International Journal of Computer Applications
%@ 0975-8887
%V 2
%N 1
%P 101-104
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper investigates the possibility estimating the direction of arrival (DOA) in a system identification perspective. The system is modeled as an autoregressive (AR) process and extended Kalman filter (EKF) is used to estimate the DOA, which forms a state of the augmented state vector of the EKF. The states generate the signals at a linearly phased array. Simulation results demonstrate the feasibility of the approach to estimate DOA to a reasonable degree of convergence especially at high SNRs.

References
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  2. Simon Haykin and Allan Steinhardt, 1992. Adaptive Radar Detection and Estimation. John Wiley &Sons.
  3. Simon Haykin and Allan Steinhardt, 1992. Adaptive Radar Detection and Estimation. John Wiley &Sons.
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  5. A.H. Jaswinski, Stochastic Processes and Filtering Theory, 1970.Academic Press.
  6. Roberto Togneri,Li Deng, “Joint state and parameter estimation for a Target _directed non-linear dynamic system model”, IEEE Trans. Signal Processing ,vol.51,.2003.
  7. K.Radhakrishnan, A. Unnikrishnan and K.G. Balakrishnan,2006. E.M based extended Kalman filter for estimation of rotor time-constant of induction motor, IEEE int. Symp. Industrial Electronics ISIE’06, Quebec, Canada.
  8. C.K. Chui and Chen, Kalman Filtering with Real- Time Applications. 1991. New York: Springer – Verlag, .
Index Terms

Computer Science
Information Sciences

Keywords

Modeling Direction of arrival Estimation

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