00000nam 2200000za 4500 9.833978 CaOODSP 20240219183459 cr ||||||||||| 170328s2016 oncd ob f000 0 eng d CaOODSP eng n-cn--- D69-39/2016E-PDF Spline probability hypothesis density filter for nonlinear maneuvering target tracking [electronic resource] / Rajiv Sithiravel ... [et al.]. [Ottawa] : Defence Research and Development Canada, [2016] p. 1743-1750 : charts (mostly col.) Caption title. Includes bibliographical references. "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. Originally published in: Piscataway, NJ : Institute of Electrical and Electronics Engineers, Inc., c2013, Conference record of the Forty-Seventh Asilomar Conference on Signals, Systems & Computers : November 3–6, 2013, Pacific Grove, California / edited by Michael B. Matthews. 1058-6393 9781479923908. gccst Military technology gccst Navigation systems Sithiravel, Rajiv. Defence R&D Canada. PDF 261 KB https://publications.gc.ca/collections/collection_2017/rddc-drdc/D69-39-2016-eng.pdf DRDC-RDDC-2016-N046