Sea Ice Tracking with a Spatially Indexed Labeled Multi-Bernoulli Filter

• Published in: 2017 20th International Conference on Information Fusion (Fusion)
• Main Authors: Olofsson, Jonatan, Veibäck, Clas, Hendeby, Gustaf
• Format: Conference Object
• Language: English
• Published: Linköpings universitet, Reglerteknik 2017
• Subjects: Sea Ice Tracking; Labeled Multi-Bernoulli Filter; Spatial Indexing; Control Engineering; Reglerteknik; Svalbard; Norway; Kongsfjord*; Kongsfjorden; Sea ice
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139786; https://doi.org/10.23919/ICIF.2017.8009672

In polar region operations, drift ice positioning and tracking is useful for both scientific and safety reasons. At its core is a Multi-Target Tracking (MTT) problem in which currents and winds make motion modeling difficult. One recent algorithm in the MTT field, employed in this paper, is the Labeled Multi-Bernoulli (LMB) filter. In particular, a proposed reformulation of the LMB equations exposes a structure which is exploited to propose a compact algorithm for the generation of the filter's posterior distribution. Further, spatial indexing is applied to the clustering process of the filter, allowing efficient separation of the filter into smaller, independent parts with lesser total complexity than that of an unclustered filter. Many types of sensors can be employed to generate detections of sea ice, and in this paper a recorded dataset from a Terrestrial Radar Interferometer (TRI) is used to demonstrate the application of the Spatially Indexed Labeled Multi-Bernoulli filter to estimate the currents of an observed area in Kongsfjorden, Svalbard. Funding Agencies:European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie [642153]; Research Council of Norway through the Centres of Excellence funding scheme [223254 - NTNU-AMOS]; Vinnova Industry Excellence Center LINK-SIC; Swedish strategic research center Security Link; Swedish Research Council through the project Scalable Kalman Filters LINK-SIC Scalable Kalman Filters MarineUAS