Advanced Copula-based Methods for Nonparametric Detection and Characterization of Wideband Radar Signals

This paper introduces advanced copula-based methods for the nonparametric detection and characterization of wideband radar signals. The research focuses on developing signal detection algorithms that are invariant to changes in the probability density function of the sounding or reflected signals, employing multiscale analysis techniques and copula-based statistics. Two primary approaches are explored: multiscale analysis using wavelet transforms and rank-based signal detection with copula-based ambiguity functions. Simulation results confirm the effectiveness of the proposed approaches. The research demonstrates that integrating rank-based methods with copula-based statistics significantly improves the detection and analysis of wideband radar signals, particularly in complex scenarios where signals exhibit intricate dependency structures. This comprehensive detection framework is well-suited for handling high-dimensional radar signal data, enhancing accuracy and reliability under varied conditions. Future work will focus on optimizing copula selection and permutation strategies to further improve performance.