### Abstract

Navigation system usually employs multiple sensors to combine the strength of individual sensors such as GPS, gyroscope, and accelerometer. However, the multiple sensor fusion for navigation objectives encounters noise and time-variant bias present in individual sensor measurement. This paper proposes an efficient method to estimate the unknown bias for cancellation in fused navigation system involving multiple sensors using an adaptive bias prototype selection employed over a bank of parallel Kalman filters. Conventional methods, which focuses on a special case of bias characteristics revolving around the semi-Markov process model is recognized for its excessive computation if the bias prototype set is chosen too large. This means that a huge discrete set of bias is needed to obtain the bias estimation accurately. Focusing on solving the problem of large bias prototypes, we propose a two-step selection process; (1) the decision part that locks on a new bias set from estimated bias covariance and (2) the balance part that regulates the newly selected bias set to enable smooth transition under inadvertent bias overshoots. Simulation results show a substantial improvement in bias estimation accuracy while maintaining a minimal computation compared to the non-adaptive randomly switching bias estimators.

Original language | English |
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Title of host publication | IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems |

Place of Publication | Piscataway, NJ, United States |

Publisher | IEEE |

Pages | 279-284 |

Number of pages | 6 |

Publication status | Published - 1999 Dec 1 |

Externally published | Yes |

Event | Proceedings of the 1999 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI'99 - Taipei, Taiwan Duration: 1999 Aug 15 → 1999 Aug 18 |

### Other

Other | Proceedings of the 1999 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI'99 |
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City | Taipei, Taiwan |

Period | 99/8/15 → 99/8/18 |

### Fingerprint

### ASJC Scopus subject areas

- Software
- Control and Systems Engineering

### Cite this

*IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems*(pp. 279-284). Piscataway, NJ, United States: IEEE.

**Efficient bias estimation method in multisensor fusion for navigation by adaptive prototype selection in a bank of Kalman filters.** / Seo, Insik; Kang, Sunmee; Chang, Chein I.; Ko, Hanseok.

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

*IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.*IEEE, Piscataway, NJ, United States, pp. 279-284, Proceedings of the 1999 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI'99, Taipei, Taiwan, 99/8/15.

}

TY - GEN

T1 - Efficient bias estimation method in multisensor fusion for navigation by adaptive prototype selection in a bank of Kalman filters

AU - Seo, Insik

AU - Kang, Sunmee

AU - Chang, Chein I.

AU - Ko, Hanseok

PY - 1999/12/1

Y1 - 1999/12/1

N2 - Navigation system usually employs multiple sensors to combine the strength of individual sensors such as GPS, gyroscope, and accelerometer. However, the multiple sensor fusion for navigation objectives encounters noise and time-variant bias present in individual sensor measurement. This paper proposes an efficient method to estimate the unknown bias for cancellation in fused navigation system involving multiple sensors using an adaptive bias prototype selection employed over a bank of parallel Kalman filters. Conventional methods, which focuses on a special case of bias characteristics revolving around the semi-Markov process model is recognized for its excessive computation if the bias prototype set is chosen too large. This means that a huge discrete set of bias is needed to obtain the bias estimation accurately. Focusing on solving the problem of large bias prototypes, we propose a two-step selection process; (1) the decision part that locks on a new bias set from estimated bias covariance and (2) the balance part that regulates the newly selected bias set to enable smooth transition under inadvertent bias overshoots. Simulation results show a substantial improvement in bias estimation accuracy while maintaining a minimal computation compared to the non-adaptive randomly switching bias estimators.

AB - Navigation system usually employs multiple sensors to combine the strength of individual sensors such as GPS, gyroscope, and accelerometer. However, the multiple sensor fusion for navigation objectives encounters noise and time-variant bias present in individual sensor measurement. This paper proposes an efficient method to estimate the unknown bias for cancellation in fused navigation system involving multiple sensors using an adaptive bias prototype selection employed over a bank of parallel Kalman filters. Conventional methods, which focuses on a special case of bias characteristics revolving around the semi-Markov process model is recognized for its excessive computation if the bias prototype set is chosen too large. This means that a huge discrete set of bias is needed to obtain the bias estimation accurately. Focusing on solving the problem of large bias prototypes, we propose a two-step selection process; (1) the decision part that locks on a new bias set from estimated bias covariance and (2) the balance part that regulates the newly selected bias set to enable smooth transition under inadvertent bias overshoots. Simulation results show a substantial improvement in bias estimation accuracy while maintaining a minimal computation compared to the non-adaptive randomly switching bias estimators.

UR - http://www.scopus.com/inward/record.url?scp=0033340467&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033340467&partnerID=8YFLogxK

M3 - Conference contribution

SP - 279

EP - 284

BT - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

PB - IEEE

CY - Piscataway, NJ, United States

ER -