Adaptive Convolution Kernels Construction Based on Unsupervised Learning for Underwater Acoustic Detection - Hao Yin; Chao Li; Haibin Wang; Jun Wang; Fan Yin; Zili Qin; Chuxian Wang (Journal of Marine Science and Engineering, 2025)
In the field of Direction of Arrival (DOA) estimation, due to the complexity of ocean noise and the limitations of using array apertures, the bearing time record (BTR) obtained by CBF typically exhibits low signal-to-noise ratio (SNR) and wide mainlobe width. To address this issue, we propose an adaptive 2D convolution kernel construction method that utilizes an improved k-means clustering algorithm to extract adaptive mainlobe visual patterns from historical BTR data as convolution kernels. Experimental results show that our method can effectively reduce noise levels within multiple target environments.