KEYWORDS: Error analysis, Synthetic aperture radar, Statistical analysis, Data analysis, Automatic target recognition, Electroluminescence, Principal component analysis, Solids, Signal to noise ratio, Analytical research
Using multivariate data analysis to estimate the classification error rates and separability between sets of data samples is
a useful tool for understanding the characteristics of data sets. By understanding the classifiability and separability of the
data, one can better direct the appropriate resources and effort to achieve the desired performance. The following report
describes our procedure for estimating the separability of given data sets. The multivariate tools described in this paper
include calculating the intrinsic dimensionality estimates, Bayes error estimates, and the Friedman-Rafsky tests.
These analysis techniques are based on previous work used to evaluate data for synthetic aperture radar (SAR) automatic
target recognition (ATR), but the current work is unique in the methods used to analyze large dimensionality sets with a
small number of samples. The results of this report show that our procedure can quantitatively measure the performance
between two data sets in both the measure and feature space with the Bayes error estimator procedure and the Friedman-
Rafsky test, respectively. Our procedure, which included the error estimation and Friedman-Rafsky test, is used to
evaluate SAR data but can be used as effective ways to measure the classifiability of many other multidimensional data
sets.
Performance of Automatic Target Recognition (ATR) algorithms for Synthetic Aperture Radar (SAR) systems relies
heavily on the system performance and specifications of the SAR sensor. A representative multi-stage SAR ATR
algorithm [1, 2] is analyzed across imagery containing phase errors in the down-range direction induced during the
transmission of the radar's waveform. The degradation induced on the SAR imagery by the phase errors is
measured in terms of peak phase error, Root-Mean-Square (RMS) phase error, and multiplicative noise. The ATR
algorithm consists of three stages: a two-parameter CFAR, a discrimination stage to reduce false alarms, and a
classification stage to identify targets in the scene. The end-to-end performance of the ATR algorithm is quantified
as a function of the multiplicative noise present in the SAR imagery through Receiver Operating Characteristic
(ROC) curves. Results indicate that the performance of the ATR algorithm presented is robust over a 3dB change in
multiplicative noise.
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