Preventing extended Kalman filter instabilities during two transponder long baseline navigation with real time fuzzy logic parameter adjustment

Jesse Lorenzo Pentzer, Benjamin Armstrong, Thomas Bean, Michael Anderson, Dean Edwards, N. Victor Schmehl

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

Through simulation and field testing of autonomous underwater vehicles (AUVs) it has been identified that when an extended Kalman filter is used with a two transponder long baseline (LBL) positioning system instabilities can occur when range updates are introduced to the filter. This paper describes two possible algorithms to prevent the instability. The instability is dependent on the location of the vehicle relative to the transponders during the measurement. The algorithms compensate for the instability by adjusting the range measurement standard deviation parameter R in the Kalman filter. The first algorithm uses the perpendicular distance from the line between transponders and the estimated position as an input to a fuzzy logic algorithm. The second algorithm uses the cosine of the angle between vectors drawn from the estimated position of the vehicle to each transponder, β, as an input. Monte Carlo results show that both methods were successful at eliminating the instability; however, the β algorithm produced better overall results.

Original languageEnglish (US)
DOIs
StatePublished - Dec 10 2009
EventOCEANS '09 IEEE Bremen: Balancing Technology with Future Needs - Bremen, Germany
Duration: May 11 2009May 14 2009

Other

OtherOCEANS '09 IEEE Bremen: Balancing Technology with Future Needs
CountryGermany
CityBremen
Period5/11/095/14/09

Fingerprint

Transponders
Extended Kalman filters
logic
Fuzzy logic
Navigation
Autonomous underwater vehicles
Kalman filters
time
simulation
Testing

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Ocean Engineering
  • Communication

Cite this

Pentzer, J. L., Armstrong, B., Bean, T., Anderson, M., Edwards, D., & Schmehl, N. V. (2009). Preventing extended Kalman filter instabilities during two transponder long baseline navigation with real time fuzzy logic parameter adjustment. Paper presented at OCEANS '09 IEEE Bremen: Balancing Technology with Future Needs, Bremen, Germany. https://doi.org/10.1109/OCEANSE.2009.5278292
Pentzer, Jesse Lorenzo ; Armstrong, Benjamin ; Bean, Thomas ; Anderson, Michael ; Edwards, Dean ; Schmehl, N. Victor. / Preventing extended Kalman filter instabilities during two transponder long baseline navigation with real time fuzzy logic parameter adjustment. Paper presented at OCEANS '09 IEEE Bremen: Balancing Technology with Future Needs, Bremen, Germany.
@conference{660836092b3840969138e6decac9af46,
title = "Preventing extended Kalman filter instabilities during two transponder long baseline navigation with real time fuzzy logic parameter adjustment",
abstract = "Through simulation and field testing of autonomous underwater vehicles (AUVs) it has been identified that when an extended Kalman filter is used with a two transponder long baseline (LBL) positioning system instabilities can occur when range updates are introduced to the filter. This paper describes two possible algorithms to prevent the instability. The instability is dependent on the location of the vehicle relative to the transponders during the measurement. The algorithms compensate for the instability by adjusting the range measurement standard deviation parameter R in the Kalman filter. The first algorithm uses the perpendicular distance from the line between transponders and the estimated position as an input to a fuzzy logic algorithm. The second algorithm uses the cosine of the angle between vectors drawn from the estimated position of the vehicle to each transponder, β, as an input. Monte Carlo results show that both methods were successful at eliminating the instability; however, the β algorithm produced better overall results.",
author = "Pentzer, {Jesse Lorenzo} and Benjamin Armstrong and Thomas Bean and Michael Anderson and Dean Edwards and Schmehl, {N. Victor}",
year = "2009",
month = "12",
day = "10",
doi = "10.1109/OCEANSE.2009.5278292",
language = "English (US)",
note = "OCEANS '09 IEEE Bremen: Balancing Technology with Future Needs ; Conference date: 11-05-2009 Through 14-05-2009",

}

Pentzer, JL, Armstrong, B, Bean, T, Anderson, M, Edwards, D & Schmehl, NV 2009, 'Preventing extended Kalman filter instabilities during two transponder long baseline navigation with real time fuzzy logic parameter adjustment' Paper presented at OCEANS '09 IEEE Bremen: Balancing Technology with Future Needs, Bremen, Germany, 5/11/09 - 5/14/09, . https://doi.org/10.1109/OCEANSE.2009.5278292

Preventing extended Kalman filter instabilities during two transponder long baseline navigation with real time fuzzy logic parameter adjustment. / Pentzer, Jesse Lorenzo; Armstrong, Benjamin; Bean, Thomas; Anderson, Michael; Edwards, Dean; Schmehl, N. Victor.

2009. Paper presented at OCEANS '09 IEEE Bremen: Balancing Technology with Future Needs, Bremen, Germany.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Preventing extended Kalman filter instabilities during two transponder long baseline navigation with real time fuzzy logic parameter adjustment

AU - Pentzer, Jesse Lorenzo

AU - Armstrong, Benjamin

AU - Bean, Thomas

AU - Anderson, Michael

AU - Edwards, Dean

AU - Schmehl, N. Victor

PY - 2009/12/10

Y1 - 2009/12/10

N2 - Through simulation and field testing of autonomous underwater vehicles (AUVs) it has been identified that when an extended Kalman filter is used with a two transponder long baseline (LBL) positioning system instabilities can occur when range updates are introduced to the filter. This paper describes two possible algorithms to prevent the instability. The instability is dependent on the location of the vehicle relative to the transponders during the measurement. The algorithms compensate for the instability by adjusting the range measurement standard deviation parameter R in the Kalman filter. The first algorithm uses the perpendicular distance from the line between transponders and the estimated position as an input to a fuzzy logic algorithm. The second algorithm uses the cosine of the angle between vectors drawn from the estimated position of the vehicle to each transponder, β, as an input. Monte Carlo results show that both methods were successful at eliminating the instability; however, the β algorithm produced better overall results.

AB - Through simulation and field testing of autonomous underwater vehicles (AUVs) it has been identified that when an extended Kalman filter is used with a two transponder long baseline (LBL) positioning system instabilities can occur when range updates are introduced to the filter. This paper describes two possible algorithms to prevent the instability. The instability is dependent on the location of the vehicle relative to the transponders during the measurement. The algorithms compensate for the instability by adjusting the range measurement standard deviation parameter R in the Kalman filter. The first algorithm uses the perpendicular distance from the line between transponders and the estimated position as an input to a fuzzy logic algorithm. The second algorithm uses the cosine of the angle between vectors drawn from the estimated position of the vehicle to each transponder, β, as an input. Monte Carlo results show that both methods were successful at eliminating the instability; however, the β algorithm produced better overall results.

UR - http://www.scopus.com/inward/record.url?scp=71249159994&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=71249159994&partnerID=8YFLogxK

U2 - 10.1109/OCEANSE.2009.5278292

DO - 10.1109/OCEANSE.2009.5278292

M3 - Paper

ER -

Pentzer JL, Armstrong B, Bean T, Anderson M, Edwards D, Schmehl NV. Preventing extended Kalman filter instabilities during two transponder long baseline navigation with real time fuzzy logic parameter adjustment. 2009. Paper presented at OCEANS '09 IEEE Bremen: Balancing Technology with Future Needs, Bremen, Germany. https://doi.org/10.1109/OCEANSE.2009.5278292