A model error formulation of the multiple model adaptive estimation algorithm

Christopher K. Nebelecky, John L. Crassidis, Puneet Singla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper presents a new form of the multiple model adaptive estimation algorithm for improved state tracking in systems with unknown system models. The proposed approach differs from existing multiple model methods in the manner in which the covariance and Kalman gains of the individual filters are calculated. By using the fused model estimate, recursions for the actual estimation error covariances are derived which account for the deviation of the hypothesized model from the fused model. Using these covariances to determine the Kalman gain leads to improved tracking estimates through fusion of model and measurement uncertainty. The proposed algorithm has been compared against the standard multiple model adaptive estimation and interacting multiple model algorithms in two simulated examples, resulting in improved, and comparable tracking performance, respectively.

Original languageEnglish (US)
Title of host publicationFUSION 2014 - 17th International Conference on Information Fusion
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788490123553
StatePublished - Oct 3 2014
Event17th International Conference on Information Fusion, FUSION 2014 - Salamanca, Spain
Duration: Jul 7 2014Jul 10 2014

Publication series

NameFUSION 2014 - 17th International Conference on Information Fusion

Other

Other17th International Conference on Information Fusion, FUSION 2014
CountrySpain
CitySalamanca
Period7/7/147/10/14

Fingerprint

Error analysis
Fusion reactions
Uncertainty

All Science Journal Classification (ASJC) codes

  • Information Systems

Cite this

Nebelecky, C. K., Crassidis, J. L., & Singla, P. (2014). A model error formulation of the multiple model adaptive estimation algorithm. In FUSION 2014 - 17th International Conference on Information Fusion [6916281] (FUSION 2014 - 17th International Conference on Information Fusion). Institute of Electrical and Electronics Engineers Inc..
Nebelecky, Christopher K. ; Crassidis, John L. ; Singla, Puneet. / A model error formulation of the multiple model adaptive estimation algorithm. FUSION 2014 - 17th International Conference on Information Fusion. Institute of Electrical and Electronics Engineers Inc., 2014. (FUSION 2014 - 17th International Conference on Information Fusion).
@inproceedings{495c634827e5434f8ab63d69e9604bd7,
title = "A model error formulation of the multiple model adaptive estimation algorithm",
abstract = "This paper presents a new form of the multiple model adaptive estimation algorithm for improved state tracking in systems with unknown system models. The proposed approach differs from existing multiple model methods in the manner in which the covariance and Kalman gains of the individual filters are calculated. By using the fused model estimate, recursions for the actual estimation error covariances are derived which account for the deviation of the hypothesized model from the fused model. Using these covariances to determine the Kalman gain leads to improved tracking estimates through fusion of model and measurement uncertainty. The proposed algorithm has been compared against the standard multiple model adaptive estimation and interacting multiple model algorithms in two simulated examples, resulting in improved, and comparable tracking performance, respectively.",
author = "Nebelecky, {Christopher K.} and Crassidis, {John L.} and Puneet Singla",
year = "2014",
month = "10",
day = "3",
language = "English (US)",
series = "FUSION 2014 - 17th International Conference on Information Fusion",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "FUSION 2014 - 17th International Conference on Information Fusion",
address = "United States",

}

Nebelecky, CK, Crassidis, JL & Singla, P 2014, A model error formulation of the multiple model adaptive estimation algorithm. in FUSION 2014 - 17th International Conference on Information Fusion., 6916281, FUSION 2014 - 17th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers Inc., 17th International Conference on Information Fusion, FUSION 2014, Salamanca, Spain, 7/7/14.

A model error formulation of the multiple model adaptive estimation algorithm. / Nebelecky, Christopher K.; Crassidis, John L.; Singla, Puneet.

FUSION 2014 - 17th International Conference on Information Fusion. Institute of Electrical and Electronics Engineers Inc., 2014. 6916281 (FUSION 2014 - 17th International Conference on Information Fusion).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A model error formulation of the multiple model adaptive estimation algorithm

AU - Nebelecky, Christopher K.

AU - Crassidis, John L.

AU - Singla, Puneet

PY - 2014/10/3

Y1 - 2014/10/3

N2 - This paper presents a new form of the multiple model adaptive estimation algorithm for improved state tracking in systems with unknown system models. The proposed approach differs from existing multiple model methods in the manner in which the covariance and Kalman gains of the individual filters are calculated. By using the fused model estimate, recursions for the actual estimation error covariances are derived which account for the deviation of the hypothesized model from the fused model. Using these covariances to determine the Kalman gain leads to improved tracking estimates through fusion of model and measurement uncertainty. The proposed algorithm has been compared against the standard multiple model adaptive estimation and interacting multiple model algorithms in two simulated examples, resulting in improved, and comparable tracking performance, respectively.

AB - This paper presents a new form of the multiple model adaptive estimation algorithm for improved state tracking in systems with unknown system models. The proposed approach differs from existing multiple model methods in the manner in which the covariance and Kalman gains of the individual filters are calculated. By using the fused model estimate, recursions for the actual estimation error covariances are derived which account for the deviation of the hypothesized model from the fused model. Using these covariances to determine the Kalman gain leads to improved tracking estimates through fusion of model and measurement uncertainty. The proposed algorithm has been compared against the standard multiple model adaptive estimation and interacting multiple model algorithms in two simulated examples, resulting in improved, and comparable tracking performance, respectively.

UR - http://www.scopus.com/inward/record.url?scp=84910662602&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84910662602&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84910662602

T3 - FUSION 2014 - 17th International Conference on Information Fusion

BT - FUSION 2014 - 17th International Conference on Information Fusion

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Nebelecky CK, Crassidis JL, Singla P. A model error formulation of the multiple model adaptive estimation algorithm. In FUSION 2014 - 17th International Conference on Information Fusion. Institute of Electrical and Electronics Engineers Inc. 2014. 6916281. (FUSION 2014 - 17th International Conference on Information Fusion).