Heterogeneity detection for hybrid STAP algorithm

Traditional STAP detectors require a secondary training data set that is target free and homogeneous with the cell under test (CUT). Hybrid detectors have been proposed for heterogeneous environments where the secondary data suffers from a statistical mismatch with respect to the interference in the CUT. These algorithms employ the generalised inner product (GIP) as a heterogeneity measure and eliminate the training data snapshots that are deemed heterogeneous. The GIP, however, does not take the presence of discretes or targets in the secondary data into account. If a target, or discrete, is present but sufficiently displaced from the signal of interest in the angle-Doppler plane, then it will not lead to any significant losses in the detection. Its presence, however, biases the GIP and leads to an undesirable rejection of the training data snapshot. This problem is examined in this paper where we propose the use of a projection-based statistic for heterogeneity detection. We show that this addresses the high target density problem.

[1]  William L. Melvin,et al.  Knowledge-based space-time adaptive processing , 1997, Proceedings of the 1997 IEEE National Radar Conference.

[2]  Raviraj S. Adve,et al.  A two stage hybrid space-time adaptive processing algorithm , 1999, Proceedings of the 1999 IEEE Radar Conference. Radar into the Next Millennium (Cat. No.99CH36249).

[3]  J.R. Guerci,et al.  Knowledge-aided adaptive radar at DARPA: an overview , 2006, IEEE Signal Processing Magazine.

[4]  W.L. Melvin,et al.  A STAP overview , 2004, IEEE Aerospace and Electronic Systems Magazine.

[5]  Daniel R. Fuhrmann,et al.  A CFAR adaptive matched filter detector , 1992 .

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

[7]  M. Wicks,et al.  Practical joint domain localised adaptive processing in homogeneous and nonhomogeneous environments. Part 2: Nonhomogeneous environments , 2000 .

[8]  Christ D. Richmond.  Statistics of adaptive nulling and use of the generalized eigenrelation (GER) for modeling inhomogeneities in adaptive processing , 2000, IEEE Trans. Signal Process..

[9]  Bernard Mulgrew,et al.  Evaluation of the single and two data set STAP detection algorithms using measured data , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[10]  E. J. Kelly.  An Adaptive Detection Algorithm , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[11]  William L. Melvin,et al.  Space-time adaptive radar performance in heterogeneous clutter , 2000, IEEE Trans. Aerosp. Electron. Syst..

[12]  William L. Melvin,et al.  An efficient architecture for nonhomogeneity detection in space-time adaptive processing airborne early warning radar , 1997 .

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

[14]  Braham Himed,et al.  Statistical analysis of the non-homogeneity detector for STAP applications , 2004, Digit. Signal Process..

[15]  B. Mulgrew,et al.  A STAP algorithm for radar target detection in heterogeneous environments , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[16]  L.E. Brennan,et al.  Theory of Adaptive Radar , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Allan Steinhardt,et al.  Improved adaptive clutter cancellation through data-adaptive training , 1999 .

[18]  Bernard Mulgrew,et al.  A hybrid STAP approach for radar target detection in heterogeneous environments , 2006, 2006 14th European Signal Processing Conference.

[19]  T. Sarkar,et al.  Compensation for the effects of mutual coupling on direct data domain adaptive algorithms , 2000 .