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Multichannel Airborne
Radar Array Data Analysis

Gerard Genello, Michael Wicks
William Melvin, and Russell Brown
USAF Rome Laboratory
26 Electronic Parkway
Griffiss AFB, NY 13441-4514
email: wicksm@lonexb.admin.rl.af.mil

Suresh Babu and Jose Torres
MITRE Corporation
Bedford, MA 01730-1420

Bruce Havlicsek
Westinghouse Corporation
Baltimore, MD 21203

Abstract This presentation provides the first analysis of multichannel measurement data, collected under the USAF Rome Laboratory MCARM (Multichannel Airborne Radar Measurement) program. We consider the impact of spatial variations in the clutter statistics on the formation of adaptive nulls in angle-Doppler space, and assess conditions under which the effects of nonhomogeneities degrade adaptive filtering such that classical radar signal processing techniques demonstrate superior performance. While it is anticipated that modern adaptive space-time processing will offer improved detection and false alarm control over these classical techniques, and is preferred whenever the adaptive signal processing design assumptions are valid, a dynamically changing statistical environment makes the judicious selection of reference data (for statistical parameter estimation) essential. As such, the sample covariance matrix must provide for a high quality estimate of the true covariance matrix describing the interference competing with target returns in the test cell of interest, if statistical signal processing for maximization of the signal-to-interference plus noise ratio is to be performed.

In order to evaluate the impact of spatially nonhomogeneous clutter returns on the performance of adaptive space-time processing algorithms, it is essential to analyze the statistical properties of measured clutter returns. As such, equalized multichannel radar data, collected using the MCARM sensor, is analyzed using the Ozturk Algorithm (a modified form of the QQ plot) to estimate the multivariate probability density function(s) which best describe the interference environment. For those regions of the surveillance volume which support adaptive space-time processing, weights are computed that depend on the measurement data, and the resulting antenna patterns compared to the output of a low sidelobe (fixed weight) beamformer. Application of these weights to measurement data for the comparison of the residual clutter-to-noise ratio in the filtered data is presented. Application of the Rome Laboratory space-time adaptive processing (RLSTAP) analysis tool for the analysis of MCARM measurement data is also presented.



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