Data association with ambiguous measurements

Matthew Travers; Todd Murphey; Lucy Pao

doi:10.1109/ACC.2008.4586765

Data association with ambiguous measurements

Matthew Travers^*, Todd Murphey, Lucy Pao

^*Corresponding author for this work

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We address the problem of tracking a single object in the neighborhood of several other closely spaced, similar objects where the sensor used to do the tracking may randomly measure the wrong object. Unlike many tracking scenarios, there is no other environmental clutter producing additional erroneous measurements. The objects move together, and the sensor provides one measurement at every time step, either due to the object of interest or due to one of the other similar nearby objects. This situation of having a "mixed" set of measurements of unknown origin occurs in real world systems. While we consider the mixed-measurement problem in an example scenario, the algorithms developed can be applied to any number of associated systems with little alteration.

Original language	English (US)
Title of host publication	2008 American Control Conference, ACC
Pages	1875-1880
Number of pages	6
DOIs	https://doi.org/10.1109/ACC.2008.4586765
State	Published - 2008
Event	2008 American Control Conference, ACC - Seattle, WA, United States Duration: Jun 11 2008 → Jun 13 2008

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

Other

Other	2008 American Control Conference, ACC
Country/Territory	United States
City	Seattle, WA
Period	6/11/08 → 6/13/08

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2008.4586765

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title = "Data association with ambiguous measurements",

abstract = "We address the problem of tracking a single object in the neighborhood of several other closely spaced, similar objects where the sensor used to do the tracking may randomly measure the wrong object. Unlike many tracking scenarios, there is no other environmental clutter producing additional erroneous measurements. The objects move together, and the sensor provides one measurement at every time step, either due to the object of interest or due to one of the other similar nearby objects. This situation of having a {"}mixed{"} set of measurements of unknown origin occurs in real world systems. While we consider the mixed-measurement problem in an example scenario, the algorithms developed can be applied to any number of associated systems with little alteration.",

author = "Matthew Travers and Todd Murphey and Lucy Pao",

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language = "English (US)",

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AB - We address the problem of tracking a single object in the neighborhood of several other closely spaced, similar objects where the sensor used to do the tracking may randomly measure the wrong object. Unlike many tracking scenarios, there is no other environmental clutter producing additional erroneous measurements. The objects move together, and the sensor provides one measurement at every time step, either due to the object of interest or due to one of the other similar nearby objects. This situation of having a "mixed" set of measurements of unknown origin occurs in real world systems. While we consider the mixed-measurement problem in an example scenario, the algorithms developed can be applied to any number of associated systems with little alteration.

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BT - 2008 American Control Conference, ACC

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Data association with ambiguous measurements

Abstract

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ASJC Scopus subject areas

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