Application of a Time-Frequency-Based Blind Source Separation to an Instantaneous Mixture of Secondary Radar Sources

In Secondary Surveillance Radar (SSR) systems, it is more difficult to locate and recognise aircrafts in the neighbourhood of civil airports since aerial traffic becomes greater. Here, we propose to apply a recent Blind Source Separation (BSS) algorithm based on Time-Frequency Analysis, in order to separate messages sent by different aircrafts and falling in the same radar beam in reception. The above source separation method involves joint-diagonalization of a set of smoothed version of spatial Wigner-Ville distributions. The technique makes use of the difference in the t-f signatures of the nonstationary sources to be separated. Consequently, as the SSR sources emit different messages at different frequencies, the above fitted to this new application. We applied the technique in simulation to separate SSR replies. Results are provided at the end of the paper.

Authors:

• M. Tria
• M. Benidir
• E. Chaumette

Keywords:

• Blind Source Separation
• Time-Frequency Analysis
• Secondary Radar

References:

[1] E. Chaumette, P. Comon and D. Muller, ICA-based technique for radiating sources estimation: application to airport surveillance, IEE Proc. F. Radar Signal Processing, 140(6), pp. 395-401.
[2] N.L.R. Petrochilos, Algorithms for Separation of Secondary Surveillance
Radar Replies, Ph. Doctorate, University of Nice (France, 2002).
[3] A. Belouchrani and M.G. Amin, Blind Source Separation Based on
Time-Frequency Signal Representations, IEEE Trans. on Signal Processing,
46 (Nov. 1998), pp. 2888-2897.
[4] C. F'evotte and C. Doncarli, Two contributions to Blind Source Separation
Using Time-Frequency Distributions, IEEE Signal Processing Letters, 11 (March. 2004), pp. 386-389.
[5] H. Krim and M. Viberg, Two decades of array signal processing research,
IEEE Sig. Proc. Magazine (July 1996), pp. 67-94.
[6] S. Haykins, Radar Array Processing, Springer-Verlag, 1993.
[7] W. Martin and P. Flandrin, Wigner-Ville spectral analysis of nonstationary processes, IEEE Trans. Acoust., Speech, Signal Processing, ASSP-33
(Dec. 1985), pp. 1461-1470.
[8] A.M. Sayeed and D.L. Jones, Optimal kernels for nonstationary spectral
estimation, IEEE Trans. Signal Processing, 43 (Feb. 1995), pp. 478-491.
[9] A. Belouchrani and K. Abed-Meraim and J-F Cardoso and E. Moulines,
A Blind Source Separation Technique Based on Second Order Statistics, IEEE Trans. on Signal Processing, 45 (Feb. 1997), pp. 434-444.
[10] C. F'evotte, Approche temps-fr'equence pour la s'eparation aveugle de
sources non-stationnaires, Ph. Doctorate, University of Nantes (France, 2003).
[11] P. Flandrin, Temps-fr'equence, Herm'es (1998), Paris.