Bearings-only tracking with biased measurements

Mónica E. Bugallo; Ting Lu; Petar M. Djurić

doi:10.1109/CAMSAP.2007.4498016

Bearings-only tracking with biased measurements

Stony Brook University

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

7 Scopus citations

Abstract

This paper focuses on particle filtering techniques for tracking a single target using bearings-only measurements. The problem is formulated as fusing information collected from two or more sensors in the presence of additive noise and multiplicative/additive biases. Assuming the biases are nuisance parameters and marginalizing them out from the estimation problem, we propose an algorithm that combines a standard particle filter and one Kalman filter to efficiently resolve the fusion problem. The algorithms are tested and compared by computer simulations which offer insight into the advantages and disadvantages of the proposed method.

Original language	English
Title of host publication	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Pages	265-268
Number of pages	4
DOIs	https://doi.org/10.1109/CAMSAP.2007.4498016
State	Published - 2007
Event	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP - St. Thomas, Virgin Islands, U.S. Duration: Dec 12 2007 → Dec 14 2007

Publication series

Name	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

Conference

Conference	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Country/Territory	Virgin Islands, U.S.
City	St. Thomas
Period	12/12/07 → 12/14/07

Keywords

Biased data
Kalman filtering
Multisensor processing
Particle filtering

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10.1109/CAMSAP.2007.4498016

Cite this

Bugallo, M. E., Lu, T., & Djurić, P. M. (2007). Bearings-only tracking with biased measurements. In 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP (pp. 265-268). Article 4498016 (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP). https://doi.org/10.1109/CAMSAP.2007.4498016

@inproceedings{e39d3ba53c8c4b78ac59ec6ce0ff88e3,

title = "Bearings-only tracking with biased measurements",

abstract = "This paper focuses on particle filtering techniques for tracking a single target using bearings-only measurements. The problem is formulated as fusing information collected from two or more sensors in the presence of additive noise and multiplicative/additive biases. Assuming the biases are nuisance parameters and marginalizing them out from the estimation problem, we propose an algorithm that combines a standard particle filter and one Kalman filter to efficiently resolve the fusion problem. The algorithms are tested and compared by computer simulations which offer insight into the advantages and disadvantages of the proposed method.",

keywords = "Biased data, Kalman filtering, Multisensor processing, Particle filtering",

author = "Bugallo, \{M{\'o}nica E.\} and Ting Lu and Djuri{\'c}, \{Petar M.\}",

year = "2007",

doi = "10.1109/CAMSAP.2007.4498016",

language = "English",

isbn = "9781424417148",

series = "2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP",

pages = "265--268",

booktitle = "2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP",

note = "2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP ; Conference date: 12-12-2007 Through 14-12-2007",

}

Bugallo, ME, Lu, T & Djurić, PM 2007, Bearings-only tracking with biased measurements. in 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP., 4498016, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP, pp. 265-268, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP, St. Thomas, Virgin Islands, U.S., 12/12/07. https://doi.org/10.1109/CAMSAP.2007.4498016

Bearings-only tracking with biased measurements. / Bugallo, Mónica E.; Lu, Ting; Djurić, Petar M.
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP. 2007. p. 265-268 4498016 (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP).

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

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T1 - Bearings-only tracking with biased measurements

AU - Bugallo, Mónica E.

AU - Lu, Ting

AU - Djurić, Petar M.

PY - 2007

Y1 - 2007

N2 - This paper focuses on particle filtering techniques for tracking a single target using bearings-only measurements. The problem is formulated as fusing information collected from two or more sensors in the presence of additive noise and multiplicative/additive biases. Assuming the biases are nuisance parameters and marginalizing them out from the estimation problem, we propose an algorithm that combines a standard particle filter and one Kalman filter to efficiently resolve the fusion problem. The algorithms are tested and compared by computer simulations which offer insight into the advantages and disadvantages of the proposed method.

AB - This paper focuses on particle filtering techniques for tracking a single target using bearings-only measurements. The problem is formulated as fusing information collected from two or more sensors in the presence of additive noise and multiplicative/additive biases. Assuming the biases are nuisance parameters and marginalizing them out from the estimation problem, we propose an algorithm that combines a standard particle filter and one Kalman filter to efficiently resolve the fusion problem. The algorithms are tested and compared by computer simulations which offer insight into the advantages and disadvantages of the proposed method.

KW - Biased data

KW - Kalman filtering

KW - Multisensor processing

KW - Particle filtering

UR - https://www.scopus.com/pages/publications/50249152960

U2 - 10.1109/CAMSAP.2007.4498016

DO - 10.1109/CAMSAP.2007.4498016

M3 - Conference contribution

AN - SCOPUS:50249152960

SN - 9781424417148

T3 - 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

SP - 265

EP - 268

BT - 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

T2 - 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

Y2 - 12 December 2007 through 14 December 2007

ER -

Bearings-only tracking with biased measurements

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Keywords

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