Corrversoria Entropy-Based Ambiguity Functions: Dual Perspective and Applications in Impulsive Noise

As a classical signal processing tool based on second-order statistics (SOS), the ambiguity function (AF) can be formulated as a time-frequency correlation (TFC) function or a quadratic time-frequency distribution (QTFD). However, AF-based methods suffer performance degradation in impulsive noise with non-Gaussian characteristics. Recent studies have incorporated impulsive noise suppression techniques to improve robustness, yet achieving both strong robustness and low computational complexity remains challenging. Drawing inspiration from corrversoria entropy as a similarity measure, two new AFs, termed corrversoria entropy-based ambiguity distribution (CoVEAD) and corrversoria entropy-based ambiguity correlation (CoVEAC), are proposed. The former serves as a robust QTFD for characterizing the time-frequency features of nonstationary signals in impulsive noise, while the latter acts as a generalized TFC that mitigates the effects of impulsive noise on signal amplitude and phase. The important properties and computational costs of the two methods are discussed. Then, based on the proposed AFs, a direction-of-arrival (DOA) estimation algorithm for narrowband linear frequency modulated (LFM) signals and a joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimation algorithm are developed. Simulation results demonstrate the superior performance of the proposed algorithms over existing methods in computational complexity and robustness, particularly in scenarios with strong impulsive noise.