Track Matching Method of Sea Surface Targets Based on Improved Longest Common Subsequence Algorithm

Abstract

Track similarity analysis is an important part of historical track big data mining analysis, which provides a basis for track clustering, target recognition, and target activity law analysis. The paper adopts the improved longest common subsequence method to measure the similarity between different tracks and complete track matching. Firstly, we establish a historical track database and establish a raster spatial index for the key sea areas of concern. Secondly, we preprocess the track data and unify the data of different sampling scales into one space-time dimension through sparse or interpolation, retrieve historical tracks that are geographically close to the current track, and use the improved longest common subsequence method to calculate the matching similarity probability between the current track and the historical track. Finally, the track clustering and activity rule mining analysis are completed according to the calculated maximum matching probability. The simulation experiment verifies that the algorithm can effectively calculate and complete the correlation matching of target tracks, which provides an effective basis for target track clustering and activity law mining and analysis.