Transient Segment Detection Based on Energy Spectrum Coefficient Entropy Posterior Probability Density

In wireless communication devices, the power envelope during the transient segment of energy-limited pulse signals effectively captures the physical characteristics of the radio frequency circuit. This unique signature can serve as a radio frequency fingerprinting, enhancing the security of the wireless communication system. A key issue in this context is accurately detecting transient signals. The existing detection algorithms can only detect the start time of transient segments, making them unable to characterize the complete transient segments. This study proposes a transient segment detection algorithm based on energy spectral coefficient entropy posterior probability density (ESCE-PPD), which can simultaneously estimate the start and end time of transient segments without relying on prior information. The effectiveness of the proposed algorithm is verified using an open source Bluetooth dataset, and its performance is compared with existing algorithms. The results demonstrate that the ESCE-PPD algorithm adds the capability to detect the end time of transient segments without increasing computational complexity and reducing anti-noise performance.