Performance Improvement of Subspace-Based Direction-Finding Algorithms Using Higher-Order Statistics

Supawat Supakwong

doi:10.15866/iree.v10i1.4956

Performance Improvement of Subspace-Based Direction-Finding Algorithms Using Higher-Order Statistics

Supawat Supakwong^(1*)

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/iree.v10i1.4956

Abstract

Traditional array signal processing techniques have been relying on the use of received signal's second-order statistic for many years. However, it suffers with some fundamental limitations. Studies of array processing based on higher-order statistic has been proposed aiming to overcome these limitations. This paper is aimed to assess the array performance enhancement when using higher-order statistic from the differential geometry perspective. Defined as the locus of all array response vectors over a set of signal parameters, the array manifold's geometrical shape and properties are known to be crucially important in characterizing the array performance. In this paper, the geometry of an array manifold associated with a higher-order statistic is investigated using of the concept of virtual sensor array. Performance analysis is presented to examine the array performance enhancement both in terms of the Cramer Rao lower bound and the array detection and resolution capabilities.
Copyright © 2015 Praise Worthy Prize - All rights reserved.

Keywords

Array Signal Processing; Subspace-Based Direction-Finding; Higher-Order Statistic; Differential Geometry

Full Text:

PDF

References

R. Schmidt, Multiple emitter location and signal parameter estimation, IEEE Transactions on Antennas and Propagation [legacy, pre - 1988], 34(3):276–280, 1986.
http://dx.doi.org/10.1109/tap.1986.1143830

R. Roy and T. Kailath, ESPRIT-estimation of signal parameters via rotational invariance techniques,Acoustics, Speech and Signal Processing, IEEE Transactions on,37(7):984–995,Jul 1989.
http://dx.doi.org/10.1109/29.32276

P. Chevalier and A Ferreol, On the virtual array concept for the fourth-order direction finding problem, Signal Processing, IEEE Transactions on, 47(9):2592–2595, Sep 1999.
http://dx.doi.org/10.1109/78.782217

B. Porat and Benjamin Friedlander, Direction finding algorithms based on high-order statistics, Signal Processing, IEEE Transactions on, 39(9):2016–2024, Sep 1991.
http://dx.doi.org/10.1109/78.134434

M.C. Dogan and J.M. Mendel, Applications of cumulants to array processing (i) aperture extension and array calibration, Signal Processing, IEEE Transactions on, 43(5):1200–1216, May 1995.
http://dx.doi.org/10.1109/78.382404

M.C. Dogan and J.M. Mendel, Applications of cumulants to array processing. (ii) non-Gaussian noise suppression. Signal Processing, IEEE Transactions on, 43(7):1663–1676, Jul 1995.
http://dx.doi.org/10.1109/78.398727

P. Chevalier, A Ferreol, and L. Albera, High-resolution direction finding from higher order statistics: The 2q- MUSIC algorithm, Signal Processing, IEEE Transactions on, 54(8):2986–2997, Aug 2006.
http://dx.doi.org/10.1109/tsp.2006.877661

P. Chevalier, L. Albera, A Ferreol, and P. Comon, On the virtual array concept for higher order array processing, Signal Processing, IEEE Transactions on, 53(4):1254–1271, April 2005.
http://dx.doi.org/10.1109/tsp.2005.843703

A. Manikas, Differential Geometry in Array Processing, Imperial College Press, London, 2004.
http://dx.doi.org/10.1002/jnm.587

A. Manikas and C. Proukakis, Modeling and estimation of ambiguities in linear arrays, IEEE Transactions on Signal Processing, 46(8):2166–2179, 1998.
http://dx.doi.org/10.1109/78.705428

Supakwong, S., Further investigations on the causes of manifold ambiguities with applications in an array formation, (2010) International Review on Modelling and Simulations (IREMOS), 3 (4), pp. 461-468.

A. Manikas, C. Proukakis, and V. Lefkaditis, Investigative study of planar array ambiguities based on “hyperhelical” parameterization, IEEE Transactions on Signal Processing, 47(6):1532–1541, 1999.
http://dx.doi.org/10.1109/78.765122

Supakwong, S., A novel selection criterion for distributed vector sensors to optimize the array's ultimate detection capability, (2010) International Review on Modelling and Simulations (IREMOS), 3 (2), pp. 206-211.

T. Willmore, An Introduction to Differential Geometry, Oxford University Press, UK, 1959.
http://dx.doi.org/10.1017/s0013091500025141

N. Takai and A Manikas, Array manifold properties and performance of higher-order signal subspace techniques, In Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on, volume 5, pages 666–669 vol.5, April 1993.
http://dx.doi.org/10.1109/icassp.1993.319900

Refbacks

There are currently no refbacks.

Username
Password
Remember me