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| 02173cam 2200265za 4500 |
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| 001 | 9.833978 |
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| 003 | CaOODSP |
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| 005 | 20221107150332 |
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| 007 | cr ||||||||||| |
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| 008 | 170328s2016 oncd ob f000 0 eng d |
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| 040 | |aCaOODSP|beng |
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| 043 | |an-cn--- |
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| 086 | 1 |aD69-39/2016E-PDF |
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| 245 | 00|aSpline probability hypothesis density filter for nonlinear maneuvering target tracking |h[electronic resource] / |cRajiv Sithiravel ... [et al.]. |
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| 260 | |a[Ottawa] : |bDefence Research and Development Canada, |c[2016] |
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| 300 | |ap. 1743-1750 : |bcharts (mostly col.) |
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| 500 | |aCaption title. |
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| 504 | |aIncludes bibliographical references. |
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| 520 | 3 |a"The Probability Hypothesis Density (PHD) filter is an efficient algorithm for multitarget tracking in the presence of nonlinearities and/or non-Gaussian noise. The Sequential Monte Carlo (SMC) and Gaussian Mixture (GM) techniques are commonly used to implement the PHD filter. Recently, a new implementation of the PHD filter using B-splines with the capability to model any arbitrary density functions using only a few knots was proposed. The Spline PHD (SPHD) filter was found to be more robust than the SMC-PHD filter since it does not suffer from degeneracy and it was better than the GM-PHD implementation in terms of estimation accuracy, albeit with a higher computational complexity. In this paper, we propose a Multiple Model (MM) extension to the SPHD filter to track multiple maneuvering targets. Simulation results are presented to demonstrate the effectiveness of the new filter.--Abstract, p. 1743. |
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| 534 | |pOriginally published in:|cPiscataway, NJ : Institute of Electrical and Electronics Engineers, Inc., c2013,|tConference record of the Forty-Seventh Asilomar Conference on Signals, Systems & Computers : November 3–6, 2013, Pacific Grove, California / edited by Michael B. Matthews.|x1058-6393|z9781479923908. |
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| 692 | 07|2gccst|aMilitary technology |
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| 692 | 07|2gccst|aNavigation systems |
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| 700 | 1 |aSithiravel, Rajiv. |
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| 710 | 1 |aCanada. |bDefence R&D Canada. |
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| 856 | 40|qPDF|s261 KB|uhttps://publications.gc.ca/collections/collection_2017/rddc-drdc/D69-39-2016-eng.pdf |
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