Application of the Global Matched Filter to Stap Data an Efficient Algorithmic Approach

The global matched filter (GMF) is a mean to solve simultaneously detection and estimation problems. It has been applied mainly in source localization and delay estimation problems where the amount of data to be handled is reasonably small. This is not the case in the space time adaptive processing (STAP) context where the amount of data is extremely large and constitutes probably the major challenge. We present a very efficient algorithm that allows to apply the GMF to problems having several thousands unknowns. This allows its application to the STAP data. We detail both the algorithm and the implementation of the GMF to the STAP data. Since the GMF is a high resolution technique it outperforms the algorithms that are traditionally used in this context

[1]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[2]  Mauricio D. Sacchi,et al.  Interpolation and extrapolation using a high-resolution discrete Fourier transform , 1998, IEEE Trans. Signal Process..

[3]  R. Klemm.  Principles of Space-Time Adaptive Processing , 2002 .

[4]  Jean-Jacques Fuchs,et al.  Recovery of exact sparse representations in the presence of bounded noise , 2005, IEEE Transactions on Information Theory.

[5]  Dmitry M. Malioutov,et al.  Homotopy continuation for sparse signal representation , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[6]  Michael Elad,et al.  Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.

[7]  James Ward,et al.  Space-time adaptive processing for airborne radar , 1994, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[8]  Bhaskar D. Rao,et al.  Signal processing with the sparseness constraint , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[9]  M. R. Osborne,et al.  A new approach to variable selection in least squares problems , 2000 .

[10]  Jean-Jacques Fuchs,et al.  On sparse representations in arbitrary redundant bases , 2004, IEEE Transactions on Information Theory.

[11]  Jean-Jacques Fuchs,et al.  On the application of the global matched filter to DOA estimation with uniform circular arrays , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[12]  Stefano Alliney,et al.  An algorithm for the minimization of mixed l1 and l2 norms with application to Bayesian estimation , 1994, IEEE Trans. Signal Process..

[13]  J. Fuchs.  More on sparse representations in arbitrary bases , 2003 .

[14]  Rémi Gribonval,et al.  Sparse representations in unions of bases , 2003, IEEE Trans. Inf. Theory.

[15]  Daniel F. Marshall,et al.  The ARPA/NAVY Mountaintop Program: adaptive signal processing for airborne early warning radar , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[16]  Richard Klemm,et al.  Introduction to space-time adaptive processing , 1998 .

[17]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[18]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[19]  A. Atkinson.  Subset Selection in Regression , 1992 .

[20]  L. E. Brennan,et al.  Adaptive arrays in airborne MTI radar , 1976 .

[21]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[22]  Balas K. Natarajan,et al.  Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..

[23]  Terry E. Ewart,et al.  Separating multipaths by global optimization of a multidimensional matched filter , 1986, IEEE Trans. Acoust. Speech Signal Process..