000 03158nam  2200373zi 4500
0019.881703
003CaOODSP
00520221107170119
006m     o  d f      
007cr cn|||||||||
008191105t20192019onca    ob   f000 0 eng d
040 |aCaOODSP|beng|erda|cCaOODSP
0410 |aeng|beng|bfre
043 |an-cn---
0861 |aD68-2/058-2019E-PDF
1001 |aMillefiori, Leonardo M., |eauthor.
24510|aPattern of life model parameterization for exploitation in command and control systems : |bmethodology report part I : target motion model and formalization / |cLeonardo M. Millefiori, Paolo Braca, Steven Horn.
264 1|aOttawa, ON : |bDefence Research and Development Canada = Recherche et développement pour la défense Canada, |c2019.
264 4|c©2019
300 |a1 online resource (vii, 41 pages, 2 unnumbered pages) : |bcolour illustrations.
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
4901 |aScientific report ; |vDRDC-RDDC-2019-R058
500 |a"Can unclassified."
500 |a"September 2019."
504 |aIncludes bibliographical references (pages 38-40).
5203 |a"Extracting valuable information from large spatio-temporal datasets requires innovative approaches that can efficiently deal with large amounts of data and, at the same time, effectively reveal the underlying structure of the data, in order to provide useful information to the decision making process. Innovative knowledge discovery techniques have been developed which use a stochastic mean-reverting modeling of the ships motion to reveal the underlying graphical structure of maritime traffic. The generated knowledge enables numerous possibilities, from graph-based multi-edge prediction to anomaly detection techniques, to ship routing optimization. Altogether, the topics covered in this report represent the theoretical framework that is required for the development of knowledge discovery techniques able to reveal the underlying graph structure of maritime traffic, which are documented in the companion report—Part II. This report—Part I—documents the formalization of the ship motion model, motivating its use over other conventional models. Procedures to estimate the process parameters are provided and its use for long-term prediction and data association isinvestigated. The main limitation of this model, its applicability to non-maneuvering targets only, is also overcome by formalizing an augmented version of the model that fits the case of a vessel navigating by waypoints. Real-world data sets are used to show the potential of the developed techniques in cases of practical relevance"--Abstract, page i.
546 |aIncludes abstracts in English and French.
69207|2gccst|aNavigation
69207|2gccst|aModels
7102 |aDefence R&D Canada. |bCentre for Operational Research and Analysis.
830#0|aScientific report (Defence R&D Canada)|vDRDC-RDDC-2019-R058.|w(CaOODSP)9.802305
85640|qPDF|s8.42 MB|uhttps://publications.gc.ca/collections/collection_2019/rddc-drdc/D68-2-058-2019-eng.pdf