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Coherent sources separation based on sparsity: an application to SSR signals

Published online by Cambridge University Press:  15 May 2009

Nicolas Petrochilos ,
Gaspare Galati  and
Emilio Piracci
Nicolas Petrochilos*
Affiliation:
JABSOM, University of Hawai'i, 1356 Lusitana Street, 7th Floor, Honolulu, HI 96813, USA.
Gaspare Galati
Affiliation:
DISP and V. Volterra Center, Tor Vergata Uni., Via del Politecnico, 1-00133 Roma, Italy.
Emilio Piracci
Affiliation:
DISP and V. Volterra Center, Tor Vergata Uni., Via del Politecnico, 1-00133 Roma, Italy.
*
Corresponding author: N. Petrochilos Email: [email protected]
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Abstract

Systems based on secondary surveillance radar (SSR) downlink signals, both with directional and with omni-directional antennae (such as in multilateration), are operational today and more and more installations are being planned. In this frame, high-density traffic leads to the reception of a mixture of several overlapping SSR replies. By nature, SSR sources are sparse, i.e. with amplitude equal to zero with significantly high probability. While in the literature several algorithms performing sources separation with an m-element antenna have been proposed, none has satisfactorily employed the full potential of sparsity for SSR signals. Most sparsity algorithms can separate only real-valued sources, although we present in this study two algorithms to separate the complex-valued SSR sources. Recorded signals in a live environment are used to demonstrate the effectiveness of the proposed techniques.

Keywords

Secondary surveillance radararray processingblind source separationsparsityair traffic control
Type
Original Article
Information
International Journal of Microwave and Wireless Technologies , Volume 1 , Special Issue 3: Surveillance systems for air and airport traffic control , June 2009 , pp. 185 - 191
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2009

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References

REFERENCES

[1]Stevens, M.C.: Secondary Surveillance Radar, Artech house, Norwood, MA, 1988.Google Scholar
[2]Trim, R.: Mode S: an introduction and overview. Electron. Commun. Eng. J., 2 (1990), 53–59.CrossRefGoogle Scholar
[3]Bezousek, P.: A passive radar surveillance system VERA for ATC, in Proc. IRS'98, Munich, Germany, 1998.Google Scholar
[4]Gatati, G.: A super-resolution processor/receiver to discriminate superimposed SSR replies and squitter, USA Patent 6,819,282 B1. EU patent 02728019-7-2220-IT 0200206, 16 November 2004.Google Scholar
[5]Petrochilos, N.; van der Veen, A.J.: Algorithms to separate overlapping secondary surveillance radar replies, in Proc. ICASSP 2004, Montreal, Canada, 17–21 May 2004, II.49–53.Google Scholar
[6]Roy, R.; Kailath, T.: ESPRIT estimation of signal parameters via rotational invariance techniques. IEEE Trans. Acoust. Speech Signal Process., 37 (1989), 984–995.CrossRefGoogle Scholar
[7]Comon, P.: Independent component analysis, a new concept?. Signal Process. special issue on Higher-Order Statist., 36 (1994), 287–314.Google Scholar
[8]Petrochilos, N.; Comon, P.: A zero-cumulant random variable and its applications. Signal Process. Mag., 86 (2006), 3334–3338.CrossRefGoogle Scholar
[9]van der Veen, A.J.; Tol, J.: Separation of zero/constant modulus signals, in Proc. IEEE ICASSP, Munich, Germany, April 1997, 3445–3448.Google Scholar
[10]Petrochilos, N.; van der Veen, A.J.: Algebraic algorithms to separate overlapping secondary surveillance radar replies. IEEE Trans. Signal Process., 55 (2007), 3746–3759.CrossRefGoogle Scholar
[11]Petrochilos, N.; Comon, P.: Link between the joint diagonalisation of symmetrical cubes and parafac: an application to secondary surveillance radar, in Proc. IEEE SAM 2006, Waltham, MA, 12–14 July 2006.Google Scholar
[12]Petrochilos, N.; Galati, G.; Mené, L.; Piracci, E.: Separation of multiple secondary surveillance radar sources in a real environment by a novel projection algorithm, in Proc. IEEE ISSPIT 2005, Athens, Greece, 17–21 December 2005.Google Scholar
[13]Petrochilos, N.; Galati, G.; Piracci, E.: Application of array processing to receiving stations of multilateration systems based on ssr signals, IEEE Trans. Aerosp. Electron. Syst., 2008, accepted.Google Scholar
[14]Zibulevsky, M.; Pearlmutter, B.; Bofill, P.; Kisilev, P.: Independent Component Analysis: Principles and Practice, chapter Blind Source Separation by Sparse Decomposition, Cambridge University Press, 2001.CrossRefGoogle Scholar
[15]Larue, A.; Van Der Baan, M.; Mars, J.; Jutten, C.: Sparsity or whiteness: what criterion to use for blind deconvolution of seismic data?. SEG Technical Program Expanded Abstracts, 2005, 1642–1645.CrossRefGoogle Scholar
[16]Bofill, P.; Zibulevsky, M.: Blind separation of more sources than mixtures using sparsity of their short-time Fourier transform, in Proc. Int. Workshop on ICA ans BSS, Helsinki. Finland, 19–22 June 2000.Google Scholar
[17]Hough, P.V.C.: Method and means for recognizing complex patterns, U.S. Patent 3,069,654. 1962.Google Scholar
[18]O'Grady, P.; Pearlmutter, B.; Rickard, S.: Survey of sparse and non-sparse methods in source separation. Int. J. Imaging Syst. Technol., 15 (2005), 18–33 (special issue on Blind Source Separation Deconvolution Imaging Image Process).CrossRefGoogle Scholar