Application of Wavelet Denoising to Improve OFDM‐based Signal Detection and Classification

The developmental emphasis on improving wireless access security through various OSI PHY layer mechanisms continues. This work investigates the exploitation of RF waveform features that are inherently unique to specific devices and that may be used for reliable device classification (manufacturer, model, or serial number). Emission classification is addressed here through detection, location, extraction, and exploitation of RF [fingerprints] to provide device‐specific identification. The most critical step in this process is burst detection which occurs prior to fingerprint extraction and classification. Previous variance trajectory (VT) work provided sensitivity analysis for burst detection capability and highlighted the need for more robust processing at lower signal‐to‐noise ratio (SNR). The work presented here introduces a dual‐tree complex wavelet transform (DT‐ℂWT) denoising process to augment and improve VT detection capability. The new method\u27s performance is evaluated using the instantaneous amplitude responses of experimentally collected 802.11a OFDM signals at various SNRs. The impact of detection error on signal classification performance is then illustrated using extracted RF fingerprints and multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. Relative to previous approaches, the DT‐ℂWT augmented process emerges as a better alternative at lower SNR and yields performance that is 34% closer (on average) to [perfect] burst location estimation performance. Abstract © 2009 John Wiley & Sons, Ltd

Power Ramped-up Preamble RF Fingerprints of Wireless Transmitters

In this paper, we propose a novel kind of RF fingerprints (RFF) with better discriminability than typical RFF for identifying preamble-based wireless transmitters. First, the equivalent model of RFF identification system is built. Then, the typical RFF are analyzed with the built model and the novel RFF, which is transformed from preamble signal when its power is ramped up, is presented. Finally, the discriminability of the proposed RFF and typical RFF is experimentally evaluated with Wi-Fi 802.11b devices. The proposed RFF can be integrated into fusion identification of preamble-based wireless devices with multiple RFF

Air Force Institute of Technology Research Report 2010

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physic