Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning
(2020) 2020 American Control Conference p.4435-4441- Abstract
- Accurate carrier-phase integer ambiguity resolu-tion is fundamental for high precision global navigation satellitesystems (GNSSs). In this paper we extend a recently proposedmixture Kalman filter solution to integer ambiguity resolution.We utilize the Fisher information matrix to project the acquiredmeasurements into a lower-dimensional subspace, formulatingan optimization program to find the projected measurementthat minimally degrades filter performance with respect to themean squared error (MSE) of the estimate. Using the projectedmeasurements, our method achieves a significant computationalspeedup while retaining the performance of the original filter.Theoretical results are presented regarding the optimal... (More)
- Accurate carrier-phase integer ambiguity resolu-tion is fundamental for high precision global navigation satellitesystems (GNSSs). In this paper we extend a recently proposedmixture Kalman filter solution to integer ambiguity resolution.We utilize the Fisher information matrix to project the acquiredmeasurements into a lower-dimensional subspace, formulatingan optimization program to find the projected measurementthat minimally degrades filter performance with respect to themean squared error (MSE) of the estimate. Using the projectedmeasurements, our method achieves a significant computationalspeedup while retaining the performance of the original filter.Theoretical results are presented regarding the optimal projec-tion computation, and the claims are subsequently illustratedby simulation examples in a Monte Carlo study. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/9e60f4ad-c18f-4a62-b5d8-551dad04fd59
- author
- Greiff, Marcus LU and Berntorp, Karl LU
- publishing date
- 2020-07-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2020 American Control Conference
- conference location
- Denver, CO, United States
- conference dates
- 2020-07-01 - 2020-07-03
- external identifiers
-
- scopus:85089594800
- DOI
- 10.23919/ACC45564.2020.9147675
- language
- English
- LU publication?
- no
- id
- 9e60f4ad-c18f-4a62-b5d8-551dad04fd59
- date added to LUP
- 2020-08-18 15:58:57
- date last changed
- 2022-04-19 00:23:36
@inproceedings{9e60f4ad-c18f-4a62-b5d8-551dad04fd59, abstract = {{Accurate carrier-phase integer ambiguity resolu-tion is fundamental for high precision global navigation satellitesystems (GNSSs). In this paper we extend a recently proposedmixture Kalman filter solution to integer ambiguity resolution.We utilize the Fisher information matrix to project the acquiredmeasurements into a lower-dimensional subspace, formulatingan optimization program to find the projected measurementthat minimally degrades filter performance with respect to themean squared error (MSE) of the estimate. Using the projectedmeasurements, our method achieves a significant computationalspeedup while retaining the performance of the original filter.Theoretical results are presented regarding the optimal projec-tion computation, and the claims are subsequently illustratedby simulation examples in a Monte Carlo study.}}, author = {{Greiff, Marcus and Berntorp, Karl}}, booktitle = {{Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning}}, language = {{eng}}, month = {{07}}, pages = {{4435--4441}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning}}, url = {{http://dx.doi.org/10.23919/ACC45564.2020.9147675}}, doi = {{10.23919/ACC45564.2020.9147675}}, year = {{2020}}, }