Offshore ship recognition based on center frequency projection of improved EMD and KNN algorithm

Abstract

Ship-radiated underwater noises are essential in marine target identification. However, it is very difficult to extract valuable target information from ocean ambient noises. Here, a combined classification method is proposed based on the improved empirical mode decomposition to process the underwater signals of offshore ships. The signals are separated into a series of samples with a signal duration of 100 ms and a set of intrinsic mode functions (IMFs) is generated for each sample. The feature extraction of each IMF is realized by defining and calculating its central frequency and energy intensity. Then, the central frequency of each IMF is projected into a three-dimensional space according to the three top-ranked IMFs based on energy intensity, and the K nearest neighbor (KNN) algorithm is employed to identify the different offshore ships. It was observed that the proposed method can efficiently distinguish the three ships when 80 % of the samples are randomly selected as the training set and the remaining 20 % are used as the test set. The overall recognition rate after 1000 random tests was greater than 95 % for the short-time signals of approximately 100 ms and completely unknown types of ships, which indicates that the proposed method is applicable to offshore ship classification and identification.