The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Kalman filtering for positioning and heading control of ships and offshore rigs
Abstract
In this article, we have described the main components of a ship motion-control system and two particular motion-control problems that require wave filtering, namely, dynamic positioning and heading autopilot. Then, we discussed the models commonly used for vessel response and showed how these models are used for Kalman filter design. We also briefly discussed parameter and noise covariance estimation, which are used for filter tuning. To illustrate the performance, a case study based on numerical simulations for a ship autopilot was considered. The material discussed in this article conforms to modern commercially available ship motion-control systems. Most of the vessels operating in the offshore industry worldwide use Kalman filters for velocity estimation and wave filtering. Thus, the article provides an up-to-date tutorial and overview of Kalman-filter-based wave filtering.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Forecasting, Structural Time Series Models and the Kalman Filter
Harvey, A. C. (1990) · doi:10.1017/cbo9781107049994 · Cited by 4,195
Cited by
A survey of dynamic positioning control systems
Sørensen, A. J. (2011) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2011.03.008 · Cited by 561
Reinforcement learning-based adaptive PID controller for DPS
Lee, D., Lee, S. J., & Yim, S. C. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.108053 · Cited by 80
Metadata
- Venue
- IEEE Control Systems
- Published
- 2009-12
- Volume
- 29
- Issue
- 6
- Pages
- 32-46
- ISSN
- 1066-033X (ISSN-L)
- 1941-000X
- OpenAlex
- W2099823866
- Cited by
- 281
- References
- 1
- FWCI
- 17.00
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Kalman filtering for positioning and heading control of ships and offshore rigs. https://app.cochat.ai/papers/10.1109/mcs.2009.934408
- The paper
- Kalman filtering for positioning and heading control of ships and offshore rigs. (2009). IEEE Control Systems. https://doi.org/10.1109/mcs.2009.934408
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
