Unifying Common Signal Analyses with Instantaneous Time-Frequency Atoms

In previous work, we presented a general framework for instantaneous time-frequency analysis but did not provide any specific details of how to compute a particular instantaneous spectrum (IS). In this work, we use instantaneous time-frequency atoms to obtain an IS associated with common signal analyses: time domain analysis, frequency domain analysis, fractional Fourier transform, synchrosqueezed short-time Fourier transform, and synchrosqueezed short-time fractional Fourier transform. By doing so, we demonstrate how the general framework can be used to unify these analyses and we develop closed-form expressions for the corresponding ISs. This is accomplished by viewing these analyses as decompositions into AM--FM components and recognizing that each uses a specialized (or limiting) form of a quadratic chirplet as a template during analysis. With a two-parameter quadratic chirplet, we can organize these ISs into a 2D continuum with points in the plane corresponding to a decomposition related to one of the signal analyses. Finally, using several example signals, we compute in closed-form the ISs for the various analyses.

WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research

Wireless network security may be improved by identifying networked devices via traits that are tied to hardware differences, typically related to unique variations introduced in the manufacturing process. One way these variations manifest is through unique transient events when a radio transmitter is activated or deactivated. Features extracted from these signal bursts have in some cases, shown to provide a unique "fingerprint" for a wireless device. However, only recently have researchers made such data available for research and comparison. Herein, we describe a publicly-available corpus of radio frequency signals that can be used for wireless device fingerprint research. The WIDEFT corpus contains signal bursts from 138 unique devices (100 bursts per device), including Bluetooth- and WiFi-enabled devices, from 79 unique models. Additionally, to demonstrate the utility of the WIDEFT corpus, we provide four baseline evaluations using a minimal subset of previously-proposed features and a simple ensemble classifier.