Maritime Surveillance with Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) Onboard a Microsatellite Constellation

New developments in small spacecraft capabilities will soon enable formation-flying constellations of small satellites, performing cooperative distributed remote sensing at a fraction of the cost of traditional large spacecraft missions. As part of ongoing research into applications of formation-flight technology, recent work has developed a mission concept based on combining synthetic aperture radar (SAR) with automatic identification system (AIS) data. Two or more microsatellites would trail a large SAR transmitter in orbit, each carrying a SAR receiver antenna and one carrying an AIS antenna. Spaceborne AIS can receive and decode AIS data from a large area, but accurate decoding is limited in high traffic areas, and the technology relies on voluntary vessel compliance. Furthermore, vessel detection amidst speckle in SAR imagery can be challenging. In this constellation, AIS broadcasts of position and velocity are received and decoded, and used in combination with SAR observations to form a more complete picture of maritime traffic and identify potentially non-cooperative vessels. Due to the limited transmit power and ground station downlink time of the microsatellite platform, data will be processed onboard the spacecraft. Herein we present the onboard data processing portion of the mission concept, including methods for automated SAR image registration, vessel detection, and fusion with AIS data. Georeferencing in combination with a spatial frequency domain method is used for image registration. Wavelet-based speckle reduction facilitates vessel detection using a standard CFAR algorithm, while leaving sufficient detail for registration of the filtered and compressed imagery. Moving targets appear displaced from their actual position in SAR imagery, depending on their velocity and the image acquisition geometry; multiple SAR images acquired from different locations are used to determine the actual positions of these targets. Finally, a probabilistic inference model combines the SAR target data with transmitted AIS data, taking into account nearest-neighbor position matches and uncertainty models of each observation.