ADAPTIVE DETECTION OF SIGNALS WITH LINEAR FEATURE MAPPINGS AND REPRESENTATIONS

In a previous paper, Reed developed a new test function for detecting a 2-D signal with limited prior information about the signal waveform and the statistical properties of clutter, This was accomplished by su bstituting a maximum likelihood estimate (MLE) of the unknown clutter covariance matrix and the MLE's of the amplitudes of the selected sign al-feature components into a maximum invariant ratio test, However, pe rformance analyses of this detector were not obtained, In this paper, the test statistic in Reed's previous paper is extended to the complex domain for application to synthetic aperture radar (SAR) imagery, The performance of the detector is studied analytically, Closed-form expr essions for the performance of the detector, under both hypotheses H-0 and H-1, are obtained. The theoretical results show that the detectab ility of the test is strongly effected by the feature mapping and sele ction techniques used to represent a signal, Here the effectiveness of a feature representation is evaluated in terms of the number of featu res needed to represent a signal and the separability of those feature s from the clutter background, The dependence of the detection probabi lity on the effectiveness of the features is quantitatively shown by a set of performance curves, The resulting analyses indicate that the d etector has the property of a constant false alarm rate (CFAR), To mak e the results of the detection performance analysis more applicable to real problems, the loss due to a ''mismatched feature'' representatio n is also studied analytically.