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An AR Model Based Robust DOA Estimation

by K.Radhakrishnan, A.Unnikrishnan
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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
  1. Pei Jung Chung, Johann F. Bohme, “Comparative convergence analysis of EM and SAGE algorithm in DOA estimation”, IEEE Trans. Signal Processing, vol.49, 2001.
  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.
  4. Shen – Shu Xiong , Zhao – Ying, “ Neural Filtering of coloured noise based on kalman filter structure”, IEEE Trans. Instrumentation and Measurement, vol. 52, June 2003.
  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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