CFP last date
20 May 2024
Reseach Article

Performance Evaluation of an Algorithm for Estimation of DOA Using Model Estimation Technique

by K.Radhakrishnan, A. Unnikrishnan, K.G Balakrishnan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 1
Year of Publication: 2010
Authors: K.Radhakrishnan, A. Unnikrishnan, K.G Balakrishnan
10.5120/27-135

K.Radhakrishnan, A. Unnikrishnan, K.G Balakrishnan . Performance Evaluation of an Algorithm for Estimation of DOA Using Model Estimation Technique. International Journal of Computer Applications. 1, 1 ( February 2010), 18-24. DOI=10.5120/27-135

@article{ 10.5120/27-135,
author = { K.Radhakrishnan, A. Unnikrishnan, K.G Balakrishnan },
title = { Performance Evaluation of an Algorithm for Estimation of DOA Using Model Estimation Technique },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 1 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 18-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number1/27-135/ },
doi = { 10.5120/27-135 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:44:03.544588+05:30
%A K.Radhakrishnan
%A A. Unnikrishnan
%A K.G Balakrishnan
%T Performance Evaluation of an Algorithm for Estimation of DOA Using Model Estimation Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 1
%P 18-24
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a model for estimating the direction of arrival (DOA) of a signal source impinging on a Uniform Linear Array(ULA). An algorithm which uses this model for estimating the delay of the signal received at two separated sensors, in a system identification perspective has been developed and its performance is compared with the results obtained through beam forming using conventional and Minimum Variance Distortionless Response(MVDR) methods. The unknown parameter, which is the phase delay at the sensors, as a result of the target presence at any bearing is estimated using the proposed method. The phase delayed signals at any sensor is generated by interpolating the samples form the previous sensor. The interpolation coefficients are estimated by considering them as part of the state vector of an Extended Kalman Filter (EKF). The EKF is used to recursively estimate the interpolation coefficients and thereby the delay. Simulation results demonstrate the feasibility of the model and the algorithm in estimating DOA both for narrowband and broadband signals. The mean of the estimates shows a reasonable degree of convergence to the true value. The variance of the estimate of the proposed method is less than that of the conventional method and very close to the MVDR method. Further it has been found that the proposed method exhibits a faster convergence.

References
  1. J. Li, P. Stoica, Zhisong Wang, “Doubly constrained Robust Capon Beamforme”, IEEE Trans. Signal Processing, Vol.52, 2004.
  2. P. J. Chung, J. F. Bohme, “Comparative Convergence Analysis of EM and SAGE Algorithms in DOA Estimation”, IEEE Trans. Signal Processing,2001.
  3. S. Haykin and A. Steinhardt, 1992 Adaptive Radar Detection and Estimation, John Wiley and Sons,
  4. H. Cox, R.M. Seskind, and M.M. Owen, “Robust Adaptive Beamforming”, IEEE Trans. Acoustics, Speech and Signal Processing, Vol.35(10), 1987.
  5. J. Capon, “High-Resolution frequency –wave number spectrum analysis”, IEEE Trans. Signal processing , Vol.57, 1969.
  6. P. Stoica, K.C. Sharman, “Maximum Likelihood Methods for Direction of Arrival Estimation”, IEEE Trans. Acoust.,Speech, Signal Processing, Vol. 38, July 1990.
  7. R. Roy, T. Kailath, “ESPRIT – Estimation of Signal Parameter via Rotational Invariance Techniques”, IEEE Trans. Signal Processing, Vol. 37, July 1989.
  8. M. Viberg , B. Otterson, T. Kailath, “Detection and Estimation in Sensor Arrays using Weighted Subspace Fitting”, IEEE Trans. Signal Processing, Vol.39, 1991.
  9. M. Viberg, B. Otterson, “Sensor Array Processing based on Subspace Fitting”, IEEE Trans. Signal Processing, Vol.39, May 1991.
  10. A. Farina, 1992 Antenna – Based Signal Processing Techniques for Radar Systems.,Artech House inc, Boston.
  11. D. Kong and J. Chun,2000 A fast DOA Tracking Algorithm Based on the Extended Kalman filter. In Proceedingsof National Aerospace and Electronics Conference, NAECON 2000.
  12. E.F. Sagiroglu,1999 Localization of Wide-band Signals via Extended Kalman Filter. In Proceedings of IEEE International Symposium on Circuits and Systems( Vol.4, Jul 1999).
  13. R.A. Mucci, “A comparison of Efficient Beamforming Algorithms”, IEEE Trans. Acoust.,Speech and Signal Processing,Vol.32,June 1984,pp. 548-558.
  14. Y. Wu, H.C. So, P.C. Ching, “Joint Time Delay and Frequency Estimation via State Space Realization”, IEEE Signal Processing Letters,Vol. 10, Nov. 2003.
  15. R.D. Rao, “Sensitivity Consideration in a State Space Model Based Harmonic Retrievel Methods”, IEEE Trans. Acoust., Speech, Signal Processing, Vol. 37, 1989.
  16. C.K. Chui and Chen,1991 Kalman Filtering with Real – Time Applications, Springer Verlag, New York.
  17. T. Lefebvre, Herman Bruynincks,Joris De Schutter , “Online Statistical Model Recognition and State Estimation for Autonomous Compliant Motion”, IEEE Trans. Systems, Man, and Cybernetics, Part C, Vol. 35, 2005.
  18. R.G. Pridham and R.A.Mucci,1979 Digital Interpolation Beamforming for lowpass and Bandpass Signals.In proceedings IEEE (Vol.67,June 1979).
  19. R. Togneri, Li Deng, “Joint State and Parameter Estimation for a Target –Directed Non-linear Dynamic Model”, IEEE Trans. Signal Processing,Vol. 51, 2003.
  20. N. Kalouptsidis, Theoridis,1993 Adaptive System Identification and Signal Processing Algorithms , Prentice Hall, New York,1993.
  21. Shen-Shu Xiong, Zhao-Ying, “Neural Filtering of Coloured Noise Based Kalman Filter Structure”, IEEE Trans. Signal Processing, Vol. 52, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Modeling Direction of arrival Estimation Extended Kalman Filter Minimum Variance Distortionless Receiver