The localization performance of matched-field processing decreases substantially for high-mismatch and high-frequency localization scenarios.Based on incoherent frequency-difference source localization technique and d...The localization performance of matched-field processing decreases substantially for high-mismatch and high-frequency localization scenarios.Based on incoherent frequency-difference source localization technique and data-driven machine learning localization method,this paper proposes a convolutional neural network localization method based on the cross-spectral density matrix of coherent frequency-difference signal.This method utilizes dual-driven modeling and data to localize mid-to high-frequency underwater sources in mismatched and noisy scenarios.For frequency-difference signals,this method performs incoherent averaging along the central frequency dimension to reduce the influence of cross-term,and stacks along the frequency-difference dimension to preserve the correlation information between different frequencies.The resulting tensor data is then used as input to a convolutional neural network for underwater source localization.This paper conducts experiments using the SwellEx-96 simulation environment and sea trial data from the 2021 South China Sea Acoustic Tomography Long-Range Experiment.The experiments show that the proposed localization algorithm is suitable for localizing medium and high frequency sources in the noise-containing and mismatch-existing marine environment,showing favorable tolerance for underwater source localization.展开更多
基金supported by the National Natural Science Foundation of China(62071429).
文摘The localization performance of matched-field processing decreases substantially for high-mismatch and high-frequency localization scenarios.Based on incoherent frequency-difference source localization technique and data-driven machine learning localization method,this paper proposes a convolutional neural network localization method based on the cross-spectral density matrix of coherent frequency-difference signal.This method utilizes dual-driven modeling and data to localize mid-to high-frequency underwater sources in mismatched and noisy scenarios.For frequency-difference signals,this method performs incoherent averaging along the central frequency dimension to reduce the influence of cross-term,and stacks along the frequency-difference dimension to preserve the correlation information between different frequencies.The resulting tensor data is then used as input to a convolutional neural network for underwater source localization.This paper conducts experiments using the SwellEx-96 simulation environment and sea trial data from the 2021 South China Sea Acoustic Tomography Long-Range Experiment.The experiments show that the proposed localization algorithm is suitable for localizing medium and high frequency sources in the noise-containing and mismatch-existing marine environment,showing favorable tolerance for underwater source localization.
