Spatially Indexed Clustering for Scalable Tracking of Remotely Sensed Drift Ice
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For operations in the Arctic, drift ice can be a major hazard. To be able to mitigate this, it is essential to know the position of viable threats. Many sensors can be employed, such as satellite Synthetic Aperture Radar (SAR), marine radar, air surveillance et cetera. At the core of the fusion of this sensor data is a Multi-Target Tracking (MTT) problem. This problem is studied in this paper through the implementation and application of the Multiple Hypothesis Tracking (MHT) algorithm. A major limiting factor in the application of multi-target tracking is scalability. A common method of handling the scaling is clustering, which separates the MHT filter into smaller independent parts. However, with growing scale, the association of sensor data to the “right” cluster can become resource intensive in itself. A method is explored, based on rectangular lower probability bounds, to efficiently index the clusters and compartmentalize the measurement update of the MHT. The method uses the bounding box of the lower probability bound of tracks and reports respectively, to perform an intersection lookup against the sensor field-of-view, efficiently selecting clusters of relevance. The method, as well as the MHT algorithm, has been implemented and published online under an open-source license. In this report, the implementation is described and tested on simulated data for statistics. Further, it is tested against data extracted from the polarimetric classification of ice using satellite imagery of the Arctic. Results show that computational speed improvements can be achieved, in comparison to the linear complexity of a naive search, but that comparable performance can be obtained using standard database lookups.