Partially Adaptive STAP using the FRACTA Algorithm

A partially adaptive space-time adaptive processor (STAP) utilizing the recently developed FRACTA algorithm is presented which significantly reduces the high computational complexity and large sample support requirements of fully adaptive STAP. Multi-window post-Doppler dimensionality reduction techniques are employed to transform the data prior to application of the FRACTA algorithm. The FRACTA algorithm is a reiterative censoring (RC) and detection algorithm which has been shown to provide excellent detection performance in nonhomogeneous interference environments. Two multi-window post-Doppler dimensionality reduction techniques are considered: PRI-staggered and adjacent-bin. The partially adaptive FRACTA algorithm is applied to the KASSPER I (knowledge-aided sensor signal processing & expert reasoning) challenge datacube. The pulse repetition interval (PRI)-staggered approach with D=6 filters per Doppler bin is found to provide the best detection performance, outperforming the fully adaptive case while simultaneously reducing the runtime by a factor of ten. Using this implementation, partially adaptive FRACTA detects 197 out of 268 targets with one false alarm. The clairvoyant processor (the covariance matrix for each range cell is known) detects 198 targets with one false alarm. In addition, the partially adaptive FRACTA algorithm is shown to be resilient to jamming, and performs well for reduced sample support situations. When compared with partially adaptive STAP using traditional sliding window processing (SWP), the runtime of partially adaptive FRACTA is 14 times faster, and the detection performance is significantly increased (SWP detects 46 out of 268 targets with one false alarm).

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

[2]  Allan Steinhardt,et al.  Multiwindow Post-Doppler Space-Time Adaptive Processing , 1994, IEEE Seventh SP Workshop on Statistical Signal and Array Processing.

[3]  James Ward,et al.  Space-time adaptive processing for airborne radar , 1998 .

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

[5]  T. Moon,et al.  Mathematical Methods and Algorithms for Signal Processing , 1999 .

[6]  William L. Melvin,et al.  Assessment of multichannel airborne radar measurements for analysis and design of space-time processing architectures and algorithms , 1996, Proceedings of the 1996 IEEE National Radar Conference.

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

[8]  B. Carlson Covariance matrix estimation errors and diagonal loading in adaptive arrays , 1988 .

[9]  K. Gerlach,et al.  Efficient robust AMF using the FRACTA algorithm , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[10]  I. Reed,et al.  Rapid Convergence Rate in Adaptive Arrays , 1974, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Ping Li,et al.  Space-time adaptive processing (STAP) with limited sample support , 2004, Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509).

[12]  Louis L. Scharf,et al.  Adaptive subspace detectors , 2001, IEEE Trans. Signal Process..

[13]  R.C. DiPietro,et al.  Extended factored space-time processing for airborne radar systems , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[14]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[15]  K. Gerlach,et al.  Robust adaptive matched filtering using the FRACTA algorithm , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Braham Himed,et al.  Performance analysis of the nonhomogeneity detector for STAP applications , 2001, Proceedings of the 2001 IEEE Radar Conference (Cat. No.01CH37200).

[17]  Karl Gerlach,et al.  Outlier resistant adaptive matched filtering , 2002 .

[18]  K. Gerlach,et al.  Errata: fast converging adaptive processor for a structured covariance matrix , 2001 .

[19]  J.S. Bergin,et al.  Adaptive thresholding of non-homogeneity detection for STAP applications , 2004, Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509).

[20]  William L. Melvin,et al.  Screening among Multivariate Normal Data , 1999 .