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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.
Permanent link to this Catalogue record:
publications.gc.ca/pub?id=9.833978&sl=0
| Department/Agency | Canada. Defence R&D Canada. |
|---|---|
| Title | Spline probability hypothesis density filter for nonlinear maneuvering target tracking / Rajiv Sithiravel ... [et al.]. |
| Publication type | Monograph |
| Language | [English] |
| Format | Electronic |
| Electronic document | |
| Note(s) | Caption title. Includes bibliographical references. |
| Publishing information | [Ottawa] : Defence Research and Development Canada, [2016] |
| Author / Contributor | Sithiravel, Rajiv. |
| Description | p. 1743-1750 : charts (mostly col.) |
| Catalogue number |
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| Departmental catalogue number | DRDC-RDDC-2016-N046 |
| Subject terms | Military technology Navigation systems |