Research on Ocean Acoustic Signal Processing based on Particle Swarm Optimization Algorithm

Marine acoustic signal processing plays a vital role in underwater communications, sonar sounding, and environmental monitoring. However, due to the complexity of the marine environment, including unpredictable noise and signal attenuation, accurate signal processing becomes a challenging task. In this work, a marine acoustic signal processing model based on Particle Swarm Optimization (PSO) algorithm is proposed, which aims to improve the accuracy and efficiency of signal processing in complex underwater environments. Specifically, the PSO algorithm works by initializing a set of candidate solutions called "particles", each representing a different combination of parameters. During the search, these particles move in multidimensional space, following the global optimal solution and their respective local optimal solutions, constantly updating their positions. In this way, the PSO algorithm can dynamically adjust the filter parameters in feature extraction and the weights in the classifier, avoid falling into local optimum, and explore the optimal parameter configuration on a larger scale. Compared with traditional optimization methods, PSO has the advantages of simple calculation, easy implementation and low dependence on parameters. Experimental results show that the signal detection accuracy of the proposed PSO-based model is improved by 15% compared with the traditional optimization method under noise conditions, and the classification accuracy is significantly improved.