面向水下无人平台的单波束声呐小目标识别算法OA

Aiming at the difficulty of small target recognition caused by the limited payload capacity of underwater unmanned platforms and the scarcity of sonar data samples,this paper proposed a single-beam sonar signal target recognition algorithm adapted to the few-shot condition.Based on the single-beam echo signal of active sonar targets,this algorithm extracted multi-dimensional time-domain and frequency-domain features of the waveform,performed effective feature selection through correlation analysis and principal component analysis for dimensionality reduction,and combined these with a random forest classifier to achieve high-precision target recognition under few-shot training sets.Test results on water tank experimental data show that compared with various methods combining multi-beam sonar images with deep learning,the proposed algorithm achieves 99.42%precision,99.39%recall,99.39%F1-score,and 99.39%accuracy with a smaller training set.The proposed method has the advantages of low computational cost,fast running speed,and strong interpretability,making it more suitable for deployment on underwater unmanned platforms with limited computing and storage resources.It provides an efficient and feasible scheme for small target recognition by underwater unmanned platforms under resource-constrained conditions.