Abstract-How accurately can deterministic modes be identified from a finite record of noisy data? In this paper we answer this question by computing the Cramer-Rae bound on the error covariance matrix of any unbiased estimator of mode parameters. The bound is computed for many of the standard parametric descriptions of a mode, including autoregressive and moving average parameters, poles and residues, and poles and zeros. Asymptotic, frequency domain versions of the CramerRao bound bring insight into the role played by poles and zeros. Application of the bound to second-and fourth-order systems illustrates the coupling between estimator errors and illuminates the influence of mode locations on our ability to identify them. Application of the bound to the estimation of an energy spectrum illuminates the accuracy of estimators that presume to resolve spectral peaks.

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This paper is an initial exploration into the effects of range resolution on Automatic Target Recognition (ATR) algorithms based on High Range Resolution (HRR) signatures. The theoretical performance of a two-class, forceddecision classifier is used to quantify the effects of radar resolution on ATR performance. The classifier employed in this study is a forced-decision instantiation of the matched subspace classifier (MSC) developed under the DARPA TRUMPETS program. The paper also examines effects of range resolution on the separability of individual HRR profiles. This work is supported by DARPA/SPO under the MSTAR Enhancements (HBTI) program and in cooperation with AFRL/SNAA.