Space-time adaptive processing (STAP) with limited sample support

A particularly active area of research in space-time adaptive processing (STAP) involves scenarios in which the sample support available for training the adaptive processor is limited. Several of these scenarios are of significant current interest. One of those scenarios is an environment in which targets are potentially so dense (relative to the sample support requirements) that they bias the weight training, thereby causing significant performance degradation of the STAP processor. Such environments include those containing roads and highways, for example. Other related problems include scenarios in which the clutter itself is not homogeneous over significant ranges, e.g. conditions where the terrain type is highly variable, urban environments, etc. One technique that addresses the low-sample support conditions described above is the parametric adaptive matched filter (PAMF). Performance of this technique and several contending. STAP approaches are demonstrated using the KASSPER challenge dataset only.

[1]  Hong Wang,et al.  On adaptive spatial-temporal processing for airborne surveillance radar systems , 1994 .

[2]  Nader M. Namazi.  Signal and image processing (SIP-96) : proceedings of the IASTED International Conference, November 11-14, 1996, Orlando, Florida - USA , 1996 .

[3]  L. Scharf,et al.  The CFAR adaptive subspace detector is a scale-invariant GLRT , 1998, Ninth IEEE Signal Processing Workshop on Statistical Signal and Array Processing (Cat. No.98TH8381).

[4]  Qingwen Zhang,et al.  Parametric adaptive matched filter for airborne radar applications , 2000, IEEE Trans. Aerosp. Electron. Syst..

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

[6]  Irving S. Reed,et al.  A new CFAR detection test for radar , 1991, Digit. Signal Process..

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

[8]  Muralidhar Rangaswamy,et al.  Further Evaluations of STAP Tests in Compound-Gaussian Radar Clutter , 2001 .

[9]  James H. Michels.  A Parametric Detection Approach Using Multichannel Processes , 1989 .

[10]  E.J. Baranoski.  Constraint optimization for partially adaptive subspace STAP algorithms , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[11]  Braham Himed,et al.  Evaluation of the normalized parametric adaptive matched filter STAP test in airborne radar clutter , 2000, Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037].

[12]  Braham Himed,et al.  Performance of STAP tests in compound-Gaussian clutter , 2000, Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop. SAM 2000 (Cat. No.00EX410).

[13]  M. Rangaswamy,et al.  A parametric multichannel detection algorithm for correlated non-Gaussian random processes , 1997, Proceedings of the 1997 IEEE National Radar Conference.

[14]  Braham Himed,et al.  Robust STAP detection in a dense signal airborne radar environment , 2004, Signal Process..