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008170328s2016    oncd    ob   f000 0 eng d
040 |aCaOODSP|beng
043 |an-cn---
0861 |aD69-39/2016E-PDF
24500|aSpline probability hypothesis density filter for nonlinear maneuvering target tracking |h[electronic resource] / |cRajiv Sithiravel ... [et al.].
260 |a[Ottawa] : |bDefence Research and Development Canada, |c[2016]
300 |ap. 1743-1750 : |bcharts (mostly col.)
500 |aCaption title.
504 |aIncludes bibliographical references.
5203 |a"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.
534 |pOriginally published in:|cPiscataway, NJ : Institute of Electrical and Electronics Engineers, Inc., c2013,|tConference record of the Forty-Seventh Asilomar Conference on Signals, Systems & Computers : November 3–6, 2013, Pacific Grove, California / edited by Michael B. Matthews.|x1058-6393|z9781479923908.
69207|2gccst|aMilitary technology
69207|2gccst|aNavigation systems
7001 |aSithiravel, Rajiv.
7102 |aDefence R&D Canada.
85640|qPDF|s261 KB|uhttps://publications.gc.ca/collections/collection_2017/rddc-drdc/D69-39-2016-eng.pdf
986 |aDRDC-RDDC-2016-N046