Spline probability hypothesis density filter for nonlinear maneuvering target tracking: 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.
|Department/Agency||Defence R&D Canada.|
|Title||Spline probability hypothesis density filter for nonlinear maneuvering target tracking|
|Electronic Document|| |
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|Number of Pages||p. 1743-1750 :|
|Departmental Catalogue Number||DRDC-RDDC-2016-N046|
|Subject Terms||Military technology, Navigation systems|
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