Multitarget Tracking Performance Analysis Using the Non-Credibility Index in the Nonlinear Estimation Framework (NEF) Toolbox

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F10%3A43896380" target="_blank" >RIV/49777513:23520/10:43896380 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/NAECON.2010.5712932" target="_blank" >http://dx.doi.org/10.1109/NAECON.2010.5712932</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NAECON.2010.5712932" target="_blank" >10.1109/NAECON.2010.5712932</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multitarget Tracking Performance Analysis Using the Non-Credibility Index in the Nonlinear Estimation Framework (NEF) Toolbox

Popis výsledku v původním jazyce

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 Kalmanfilter (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 unifi

Název v anglickém jazyce

Multitarget Tracking Performance Analysis Using the Non-Credibility Index in the Nonlinear Estimation Framework (NEF) Toolbox

Popis výsledku anglicky

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 Kalmanfilter (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 unifi

Druh

D - Stať ve sborníku

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceeding of National Aerospace and Electronics Conference 2010

ISBN

978-1-4244-6578-1

ISSN

0547-3578

e-ISSN

—

Počet stran výsledku

9

Strana od-do

107-115

Název nakladatele

Institute of Electrical and Electronics Engineers ( IEEE )

Místo vydání

345 E 47TH ST, NEW YORK, NY 10017 USA

Místo konání akce

Fairborn, USA

Datum konání akce

14. 7. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000294969100018