Performance Improvement of Energy Criterion Method Based on the Instantaneous Phase Characteristics in Wi-Fi Transient Detection for RF Fingerprinting

Transient detection is recognized as a crucial component in Radio Frequency Fingerprinting (RFF) systems, particularly in transient-based approaches. Many existing algorithms rely on prior knowledge of signal behavior, which constrains their applicability to previously unseen devices. To address this limitation, an improved phase-based energy criterion (iEC- $\phi $ ) method is proposed for accurately detecting transient start points in Wi-Fi devices without requiring any device-specific information. The method is evaluated on a diverse dataset of Wi-Fi signals collected from multiple device brands under various noise conditions. Its performance is comparatively analyzed against high-accuracy and low-complexity detection methods, and competitive results are obtained in most scenarios. Furthermore, the requirement for manual threshold tuning is eliminated. The method also demonstrates robust adaptability to diverse transient patterns, highlighting its potential for real-world RFF applications.