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.
Lien permanent pour cette publication :
publications.gc.ca/pub?id=9.833978&sl=1
Ministère/Organisme | Canada. Defence R&D Canada. |
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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 |
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Numéro de catalogue du ministère | DRDC-RDDC-2016-N046 |
Descripteurs | Military technology Navigation systems |