About: Multitarget Tracking Performance Analysis Using the Non-Credibility Index in the Nonlinear Estimation Framework (NEF) Toolbox     Goto   Sponge   Distinct   Permalink

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  • Target tracking, nonlinear control, and fault detection are typically evaluated with only a Root Mean Square (RMS). RMS is an absolute measurement of the system performance and does not provide a statistic as to the tracker, controller, or fault detection algorithmic performance. For this paper, we investigate the noncredibility index (NCI) and average normalized estimation error square (ANEES) for nonlinear estimation for the Kalman Filter (KF), the Central Difference Filter (DD1), the unscented Kalman filter (UKF), and the particle filter (PF). Fault detection and target track performance is dependent on target maneuvers, sensor errors, model parameters, and state estimation which need to be understood relative to the filter performance versus the absolute performance (i.e. root mean square) of the system. Utilizing the developments of the Nonlinear Estimation Framework (NEF) toolbox, we develop methods of nonlinear relative comparison performance between nonlinear filters in a unified scenario.
  • Target tracking, nonlinear control, and fault detection are typically evaluated with only a Root Mean Square (RMS). RMS is an absolute measurement of the system performance and does not provide a statistic as to the tracker, controller, or fault detection algorithmic performance. For this paper, we investigate the noncredibility index (NCI) and average normalized estimation error square (ANEES) for nonlinear estimation for the Kalman Filter (KF), the Central Difference Filter (DD1), the unscented Kalman filter (UKF), and the particle filter (PF). Fault detection and target track performance is dependent on target maneuvers, sensor errors, model parameters, and state estimation which need to be understood relative to the filter performance versus the absolute performance (i.e. root mean square) of the system. Utilizing the developments of the Nonlinear Estimation Framework (NEF) toolbox, we develop methods of nonlinear relative comparison performance between nonlinear filters in a unified scenario. (en)
Title
  • Multitarget Tracking Performance Analysis Using the Non-Credibility Index in the Nonlinear Estimation Framework (NEF) Toolbox
  • Multitarget Tracking Performance Analysis Using the Non-Credibility Index in the Nonlinear Estimation Framework (NEF) Toolbox (en)
skos:prefLabel
  • Multitarget Tracking Performance Analysis Using the Non-Credibility Index in the Nonlinear Estimation Framework (NEF) Toolbox
  • Multitarget Tracking Performance Analysis Using the Non-Credibility Index in the Nonlinear Estimation Framework (NEF) Toolbox (en)
skos:notation
  • RIV/49777513:23520/10:43896380!RIV12-MSM-23520___
http://linked.open...avai/riv/aktivita
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  • S
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  • 273207
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  • RIV/49777513:23520/10:43896380
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  • Track metrics, RMS, non-credibility index, Relative track metrics, ANEES, PF, UKF, PDAF (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [26A444D638BF]
http://linked.open...v/mistoKonaniAkce
  • Fairborn, USA
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  • 345 E 47TH ST, NEW YORK, NY 10017 USA
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  • Proceeding of National Aerospace and Electronics Conference 2010
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  • Straka, Ondřej
  • Šimandl, Miroslav
  • Duník, Jindřich
  • Blasch, Erik
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000294969100018
http://linked.open.../riv/zahajeniAkce
issn
  • 0547-3578
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/NAECON.2010.5712932
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  • Institute of Electrical and Electronics Engineers ( IEEE )
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  • 978-1-4244-6578-1
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  • 23520
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