A scenario-adaptive blind channel estimation method for non-cooperative click-mimicking communication signals

Blind channel estimation (BCE) is essential for recognizing non-cooperative bio-inspired communication signals. However, click-mimicking signals, a typical category of cetacean-inspired communication signals, exhibit short duration, wide bandwidth, and non-stationary characteristics, leading to limited performance of cross-relation model-based methods and source signal reconstruction methods. To address this issue, this paper proposes a scenario-adaptive blind channel estimation method based on orthogonal matching pursuit (OMP) for click-mimicking signals. Underwater acoustic channels are classified into short-delay and long-delay scenarios according to whether at least one delay interval between adjacent propagation paths exceeds the signal duration, and dedicated estimation methods are developed for each scenario. For short-delay channels, the BCE problem is formulated as a sparse vector recovery model by exploiting cross-relations and multipath sparsity in single-input multiple-output systems. The model is improved through measurement vector selection, dictionary matrix optimization, and an improved OMP algorithm to enhance estimation accuracy. For long-delay channels, a source signal estimation approach is proposed based on cross-correlation and autocorrelation properties of multichannel received signals. The BCE problem is transformed into a channel recovery model with a known signal, which is solved using OMP to reduce computational complexity. Simulation and experimental results demonstrate the robustness and accuracy of the proposed method.