This paper presents a novel approach to fuse Synthetic Aperture Radar (SAR) images and Automatic Identification System (AIS) data for maritime surveillance. The procedure consists of four steps. First, ship detection is performed in the SAR image using a Constant False Alarm Rate (CFAR) algorithm; then feature extraction (ship position, heading and size) is performed on ships detected in the SAR image, the third step consists in identifying the detected ships and extracting the same features from the AIS data. The final step is to feed the fusion block with both features vectors extracted separately from the SAR and AIS. Here the arithmetic mean function is established. The algorithm is tested using simulated SAR images and AIS data. Preliminary results of the fusion of SAR and AIS data are presented and discussed.
This paper provides a novel approach for the fusion of Synthetic Aperture Radar (SAR) images and Automatic Identification System (AIS) data for the tracking of vessels over sea areas. At this aim, SAR and AIS data are simulated and optimized for the upcoming NovaSAR-S maritime and stripmap modes. These simulated data are used to test the proposed tracking methodology in real time scenario. The results also give practical guidelines on how to task NovaSAR-S to cover uncooperative vessels over the revisit time of the satellite considering the Doppler shift due to the radial velocity of the target.