Spline Probability Hypothesis Density Filter for Nonlinear Maneuvering Target Tracking

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Authors
  1. Sithiravel, R.
  2. Chen, X.
  3. McDonald, M.
  4. Kirubarajan, T.
Corporate Authors
Defence Research and Development Canada, Ottawa Research Centre, Ottawa ON (CAN)
Abstract
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.
Keywords
maneuvering target tracking;nonlinear filtering;probability hypothesis density filter
Report Number
DRDC-RDDC-2016-N046 — External Literature
Date of publication
25 Oct 2016
Number of Pages
8
DSTKIM No
CA043250
CANDIS No
804549
Format(s):
Electronic Document(PDF)

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