Situational Assessment Using Indicator Kriging for Fleet Tracking and Prediction

Maritime fleet tracking is a critical piece of naval operations. Leveraging the inherent spatial and temporal autocorrelation of vessels in a fleet, we use spatio-temporal Kriging, an interpolation technique, to estimate the likelihood of finding a vessel at a specific location. This estimation is based solely on the current and/or past locations of other vessels within the fleet. We do this by first fitting covariance models to observed fleet movements. We then use the spatio-temporal indicator Kriging to forecast the locations of vessels in a fleet at different times, with or without new information. Our results indicate a notable improvement in accuracy, ranging from 60 to 90\% compared to a baseline model. We measure accuracy using ROC AUC values. Furthermore, our study reveals that tracking only a subset of vessels within a fleet significantly enhances understanding of the entire fleet's movements. However, the number of vessels that need to be tracked increases as we move further from the last observation of the entire fleet. Future extensions of our work include integrating additional situational information, using other spatio-temporal interpolation techniques, and expanding its application beyond maritime fleets.

Esther Jose is a PhD candidate in Operations Research at the University at Buffalo, The State University of New York, where she is advised by Dr. Rajan Batta. Her research interest lies in Applied Operations Research. Most recently, her research has focused on military and security applications, particularly in optimizing information collection from satellites or from ground sensors. She also has experience in applying Operations Research to the mitigation and suppression of natural disasters, particularly wildfires. Esther is passionate about integrating equity and inclusion into her work whenever possible.