Intelligent marine area supervision based on AIS and radar fusion

A weighted track fusion algorithm is proposed to address the complexity of conventional sea monitoring data and the lack of effective and convenient monitoring methods. The new algorithm uses local information entropy and fuses data from the automatic identification system (AIS) and X-band radar. The new model employs ship monitoring data to detect five types of abnormal ship behavior: speed, course, position, ship spacing, and time abnormalities. The method weights various factors using a weight matrix built up from a distance difference matrix between different track points and calculations of the distance information entropy and residual degree using information entropy theory. We tested the proposed weighted track fusion algorithm the fusion accuracy using actual data for 2813 ships, AIS, and radar track points based on local information entropy. The resulting correlation accuracy was 95.54% for 2716 ship track points and a correlation accuracy of 90.72% for 97 ship tracks. The results indicate that the proposed algorithm is suitable for fusing multiple tracks and is more robust than traditional fusion methods.