Two-Dimensional Parameters Estimation

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Shiv Gehlot 1 Harish Parthasarathy 1 Ravendra Singh 1

1. Department of Electronics & Communication Engineering, Netaji Subash Institute of Technology, New-Delhi, India

* Corresponding author.


Received: 18 May 2016 / Revised: 23 Jun. 2016 / Accepted: 11 Aug. 2016 / Published: 8 Sep. 2016

Index Terms

Direction of arrival, high resolution, two-dimensional, velocity estimation


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. 

Cite This Paper

Shiv Gehlot, Harish Parthasarathy, Ravendra Singh,"Two-Dimensional Parameters Estimation", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.9, pp.1-9, 2016. DOI: 10.5815/ijigsp.2016.09.01


[1]Hamid Karim and Mats Viberg, “Two Decade of Array Signal Processing,”IEEE Signal Processing Magazine, July 1996.
[2]Schmidt, R.O, "Multiple Emitter Location and Signal Parameter Estimation," IEEE Trans. Antennas Propagation, Vol. AP-34 (March 1986), pp.276-280.
[3]Belouchrani, A.; Amin, M.G., "Time-frequency MUSIC," Signal Processing Letters, IEEE , vol.6, no.5, pp.109,110, May 1999.
[4]Paulraj, A.; Roy, R.; Kailath, T., "A subspace rotation approach to signal parameter estimation," Proceedings of the IEEE , vol.74, no.7, pp.1044,1046, July 1986.
[5]Roy, R.; Kailath, T., "ESPRIT-estimation of signal parameters via rotational invariance techniques," Acoustics, Speech and Signal Processing, IEEE Transactions on , vol.37, no.7, pp.984,995, Jul 1989.
[6]GIRD Systems, Inc. 310 Terrace Ave. Cincinnati, Ohio 45220, “An Introduction to MUSIC and ESPRIT”.
[7]Lavate, T.B.; Kokate, V.K.; Sapkal, A.M., "Performance Analysis of MUSIC and ESPRIT DOA Estimation Algorithms for Adaptive Array Smart Antenna in Mobile Communication," Computer and Network Technology (ICCNT), 2010 Second International Conference on , vol., no., pp.308,311, 23-25 April 2010.
[8]H.L. Van Trees, Detection, Estimation, and Modulation Theory, New York, Wiley 1971.
[9]Myung, In Jae. "Tutorial on maximum likelihood estimation," Journal of mathematical Psychology 47.1 (2003): 90-100.
[10]S.K. Gehlot, Ravendra Singh, “Trajectory Estimation of a moving charged particle: An application of least square estimation (LSE) approach”, IEEE, Medcom 2014, pp 262-265, Nov 2014.
[11]Saea A. Van De Geer, Least Squares Estimation, Encyclopaedia of Statistics in Behavioral Science, Volume 2, pp. 10411045.
[12]C. Radhakrishnan Rao. Linear Statistical Inference and Its Applications,John Wiley Sons 1965.
[13]H. L. Van Trees, “Optimum array processing – Part IV of detection, estimation, and modulation theory”, John Wiley, 2002
[14]Gholami, M.R.; Gezici, S.; Strom, E.G.; Rydstrom, M., "Positioning algorithms for cooperative networks in the presence of an unknown turn-around time," Signal Processing Advances in Wireless Communications (SPAWC), 2011 IEEE 12th International Workshop on , vol., no., pp.166,170, 26-29 June 2011.
[15]Voinov, Vassily ; Nikulin, Mikhail (1993). Unbiased estimators and their applications. 1: Univariate case. Dordrect: Kluwer Academic Publishers. ISBN 0-7923-2382-3.
[16]Zachariah, D.; Stoica, P., "Cramer-Rao Bound Analog of Bayes' Rule [Lecture Notes]," Signal Processing Magazine, IEEE , vol.32, no.2, pp.164,168, March 2015.
[17]Frieden, B. Roy. Science from Fisher information: a unification. Cambridge University Press, 2004.