Accession Number:

ADA564383

Title:

Toward an Integrated Framwork for Data-Efficient Parametric Adaptive Detection

Descriptive Note:

Final rept. 15 Apr 2009-30 Nov 2011

Corporate Author:

STEVENS INST OF TECHNOLOGY HOBOKEN NJ

Personal Author(s):

• Li, Hongbin

Report Date:

2012-02-27

Pagination or Media Count:

47.0

Abstract:

The conjugate-gradient CG algorithm is investigated for reduced-rank STAP detection. A family of CG matched filter CG-MF is developed by using the k-th iteration of the CG in solving the Wiener-Hopf equation. The performance the CG-MF detectors is examined for two cases. The first involves an arbitrary covariance matrix. It is shown that each CG-MF detector 1 yields the highest output SINR and smallest MSE among all linear solutions in the Krylov subspace and 2 is CFAR with non-decreasing detection probability as k increases. The second is a structured case frequently encountered in practice, where the covariance matrix contains a rank-r component due to dominant interference sources, a scaled identity due to the presence of white noise, and a perturbation component containing the residual interference andor due to the estimation error. It is shown via a perturbation analysis that the r1-st CG-MF detector achieves an output SINR nearly identical to that of the optimum MF detector which requires full iterations of the CG algorithm. Finally, the CG algorithm is used to solve a linear prediction problem in the parametric adaptive matched filter PAMF. It is shown that the PAMF can be casted within the framework of reduced-rank STAP detection.

Descriptors:

• *PARAMETRIC ANALYSIS
• ADAPTIVE FILTERS
• ALGORITHMS
• COVARIANCE
• DETECTORS
• ERRORS
• ESTIMATES
• INTERFERENCE
• ITERATIONS
• LINEAR SYSTEMS
• LINEARITY
• MATCHED FILTERS
• MATRICES(MATHEMATICS)
• OPTIMIZATION
• OUTPUT
• PERTURBATIONS
• PREDICTIONS
• RESIDUALS
• SOLUTIONS(GENERAL)
• SOURCES
• WHITE NOISE
• YIELD

Subject Categories:

• Theoretical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE