Detection and Parameter Estimation for Polarization-Agile Signals Based on Dynamic Characteristics Matching

Most existing polarization-based spectrum sensing techniques focus on static polarized signals, but their performance is significantly affected by polarization-agile signals due to their changing polarization states. To address this, a polarization agile signal detection and parameter estimation method based on dynamic features is proposed. First, we develop a dual-polarized antenna model for these signals and create an analysis framework that includes both linear and circular polarization basis. By analyzing the polarization distribution difference factor (PDDF), we find that spikes in the PDDF's sliding-window standard deviation correspond to moments of polarization transition. Based on this, a pulse sequence pattern matching algorithm is proposed to effectively track and detect polarization-agile signals. It also has low computational complexity, making it suitable for real-time spectrum sensing requirements.