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Technical Session 1: Radar Data and Signal Processing

  • Cochair: Dr. Mike Wicks, Air Force Research Laboratory/SN
  • Cochair: Dr. Russ Lefevre, Technology Service Corporation

Tuesday Morning April 23, 2002

1.1:
Bistatic STAP: Application to Airborne Radar
 
1.2:
Optimal Classification of Polarimetric SAR Images Using Segmentation
 
1.3:
Frequency Estimation Accuracy of Rocket
1.1 Bistatic STAP: Application to Airborne Radar
By: William L. Melvin and Michael J. Callahan
 
Georgia Tech Research Institute
and: Michael C. Wicks
 
AFRL, Sensors Directorate		Abstract: In this paper we investigate bistatic STAP performance. We show that typical bistatic clutter environments appear non-stationary. Non-stationary behavior exacerbates STAP implementation. In the absence of corrective measures, SINR losses due to covariance estimation error approaches 30 dB for the numerical examples considered herein. Using localized STAP processing coupled with a time-varying weight procedure, we show that much of the performance loss can be restored.
1.2 Optimal Classification of Polarimetric SAR Images Using Segmentation
By: Pierfrancesco Lombardo
 
University of Rome "La Sapienza"
and: Christopher J. Oliver
 
N.A. Software-Liverpool, UK		Abstract: The paper presents an optimised polarimetric segmentation technique for synthetic aperture radar (SAR) images, based on a generalised maximum likelihood approach. A full theoretical derivation is presented, together with a closed form analytical performance evaluation. The technique is compared to other known polarimetric segmentation schemes by application to a polarimetric SAR image of agricultural areas. A complete characterisation of the technique is provided in terms of polarimetric sensitivity and memory requirements.
1.3 Frequency Estimation Accuracy of Rocket
By: Hanna E. Witzgall, William C. Ogle, and J. Scott Goldstein
 
Science Applications International Corp.		Abstract: We assess the frequency estimation accuracy of the recently introduced reduced rank autoregressive linear predictor called Reduced Order Correlation Kernel Estimation Technique (ROCKET). We compare the frequency estimation performance of ROCKET to both the conventional full rank autoregressive (FR-AR) method and the theoretical limit imposed by the Cramer-Rao bound (CRB). The analysis includes estimation accuracy as a function of signal-to-noise ratio (SNR), data length, and subspace rank. Simulations reveal that ROCKET can approach the CRB for a much greater range of SNR levels and for shorter data sequences than FR-AR. Perhaps more importantly, ROCKET's performance is shown to be very robust to subspace rank selection. This means that a priori knowledge of the upperbound of the number of frequencies present is not crucial to this reduced rank algorithm. Finally, it is shown that a small frequency estimation bias appears when the subspace rank is well below the signal rank.

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