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There has been a recent shift toward improving wireless access security within the OSI PHY layer by exploiting RF features that are inherently device specific and difficult to replicate by an unintended party. This work addresses the extraction and exploitation of RF "fingerprints" to classify emissions and provide device-specific identification. Burst transient detection precedes RF fingerprint extraction and is generally the most critical step in the overall process. This work provides a much needed sensitivity analysis of burst detection capability. The analysis is conducted using instantaneous amplitude responses with both Fractal-Bayesian Step Change Detection (Fractal-BSCD) and Variance Trajectory (VT) processes. The performance of each method is evaluated under varying SNR conditions using experimentally collected 802.11a OFDM signals. The impact of transient detection error on signal classification performance is then demonstrated using RF fingerprints and Multiple Discriminant Analysis (MDA) with Maximum Likelihood (ML) classification. The VT technique emerges as the better alternative for all SNRs considered and yields MDA-ML classification accuracy that is consistent with "perfect" transient estimation performance.