Spline probability hypothesis density filter for nonlinear maneuvering target tracking / Rajiv Sithiravel ... [et al.]. : D69-39/2016E-PDF

"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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publications.gc.ca/pub?id=9.833978&sl=1

Renseignements sur la publication
Ministère/Organisme Canada. Defence R&D Canada.
Titre Spline probability hypothesis density filter for nonlinear maneuvering target tracking / Rajiv Sithiravel ... [et al.].
Type de publication Monographie
Langue [Anglais]
Format Électronique
Document électronique
Note(s) Caption title.
Includes bibliographical references.
Information sur la publication [Ottawa] : Defence Research and Development Canada, [2016]
Auteur / Contributeur Sithiravel, Rajiv.
Description p. 1743-1750 : charts (mostly col.)
Numéro de catalogue
  • D69-39/2016E-PDF
Numéro de catalogue du ministère DRDC-RDDC-2016-N046
Descripteurs Military technology
Navigation systems
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