Rapid Underwater Moving Obstacle Detection Based on Joint Entropy Under Dense Collision Interference

Faced with the dilemma of dense underwater collision avoidance near ports, the lack of small moving obstacle detection strategy can easily lead to the increase of crash risk. Hence, focusing on the application of active sonar, a short-time moving obstacle extraction algorithm based on joint conditional entropy synergy is proposed. On the basis of beamforming, this method combines low-rank matrix factorization (LMF) and variational modal decomposition (VMD) to rapid achieve single frame reverberation and dynamic background suppression. Aiming at screening out moving obstacles with weak echoes, a matching model based on joint conditional entropy similarity measure is derived, and the distance and direction of moving obstacles (such as divers) are extracted by matching the sparse matrix of reference frame and current frame. This method significantly reduces the computational overhead of reverberation suppression in multi-frame LMF, and effectively alleviates the issue of missing detection of small targets. Experimental results verify the reliability and accuracy of the proposed algorithm in quickly detecting small moving obstacles.