Random-Point-Based Filters: Analysis and Comparison in Target Tracking

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43925656" target="_blank" >RIV/49777513:23520/15:43925656 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TAES.2014.130136" target="_blank" >http://dx.doi.org/10.1109/TAES.2014.130136</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TAES.2014.130136" target="_blank" >10.1109/TAES.2014.130136</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Random-Point-Based Filters: Analysis and Comparison in Target Tracking

Popis výsledku v původním jazyce

This paper compares state estimation techniques for nonlinear stochastic dynamic systems, which are important for target tracking. Recently, several methods for nonlinear state estimation have appeared utilizing various random-point-based approximationsfor global filters (e.g., particle filter and ensemble Kalman filter) and local filters (e.g., Monte-Carlo Kalman filter and stochastic integration filters). A special emphasis is placed on derivations, algorithms, and commonalities of these filters. Allfilters described are put into a common framework, and it is proved that within a single iteration, they provide asymptotically equivalent results. Additionally, some deterministic-point-based filters (e.g., unscented Kalman filter, cubature Kalman filter, and quadrature Kalman filter) are shown to be special cases of a random-point-based filter. The paper demonstrates and compares the filters in three examples, a random variable transformation, re-entry vehicle tracking, and bearings-o

Název v anglickém jazyce

Random-Point-Based Filters: Analysis and Comparison in Target Tracking

Popis výsledku anglicky

This paper compares state estimation techniques for nonlinear stochastic dynamic systems, which are important for target tracking. Recently, several methods for nonlinear state estimation have appeared utilizing various random-point-based approximationsfor global filters (e.g., particle filter and ensemble Kalman filter) and local filters (e.g., Monte-Carlo Kalman filter and stochastic integration filters). A special emphasis is placed on derivations, algorithms, and commonalities of these filters. Allfilters described are put into a common framework, and it is proved that within a single iteration, they provide asymptotically equivalent results. Additionally, some deterministic-point-based filters (e.g., unscented Kalman filter, cubature Kalman filter, and quadrature Kalman filter) are shown to be special cases of a random-point-based filter. The paper demonstrates and compares the filters in three examples, a random variable transformation, re-entry vehicle tracking, and bearings-o

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GC13-07058J" target="_blank" >GC13-07058J: Konzervativní fúze v systémech odhadu propojených v síti</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Aerospace and Electronic Systems

ISSN

0018-9251

e-ISSN

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Svazek periodika

51

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

1403-1421

Kód UT WoS článku

000356969500049

EID výsledku v databázi Scopus

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