Research on contour construction of underwater obstacle based on GAN-SVC
Aiming at the problem that the reliability of underwater unmanned vehicle(UUV)sonar detection data decreases due to complex noise,which leads to the inaccuracy of obstacle contour construction,an underwater obstacle contour construction algorithm based on generative adversarial network (GAN )and support vector clustering(SVC)is proposed. In order to distinguish complex noise points from obstacle points,the algorithm preliminarily screens outliers in sonar data based on SVC. Aiming at the problem that SVC may be affected by parameters and may cause misjudgment of small clusters,GAN is used to accurately screen outliers;and accurate obstacle points are clustered to obtain the optimal contour of each obstacle. Through constructing simulation verification experiments on contour of obstacle detection data in lake,the results show that compared with the accuracy of the proposed GAN-SVC algorithm is 79.80％ and 48.13％ higher than that of the SVC algorithm.
generative adversarial networksupport vector clusteringoutlier detectioncontour construction
万方数据
维普
