The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Kalman Filtering for Spacecraft Attitude Estimation
Abstract
HIS report reviews the methods of Kalman filtering in attitude estimation and their development over the last two decades. This review is not intended to be complete but is limited to algorithms suitable for spacecraft equipped with three-axis gyros as well as attitude sensors. These are the systems to which we feel that Kalman filtering is most ap- plicable. The Kalman filter uses a dynamical model for the time development of the system and a model of the sensor measurements to obtain the most accurate estimate possible of the system state using a linear estimator based on present and past measurements. It is, thus, ideally suited to both ground-based and on-board attitude determination. However, the applicability of the Kalman filtering technique rests on the availability of an accurate dynamical model. The dynamic equations for the spacecraft attitude pose many difficulties in the filter modeling. In particular, the external torques and the distribution of momentum internally due to the use of rotating or rastering instruments lead to significant uncertainties in the modeling. For autonomous spacecraft the use of inertial reference units as a model replacement permits the circumvention of these problems. In this representation the angular velocity of the spacecraft is obtained from the gyro data. The kinematic equations are used to obtain the attitude state and this is augmented by means of additional state-vector components for the gyro biases. Thus, gyro data are not treated as observations and the gyro noise appears as state noise rather than as observation noise. It is theoretically possible that a spacecraft is three-axis stabilized with such rigidity that the time development of the system can be described accurately without gyro information, or that it is one-axis stabilized so that only a single gyro is needed to provide information on the time history of the system. The modification of the algorithms presented here in order to apply to those cases is slight. However, this is of little practical importance because a control system capable of such
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
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Spacecraft Attitude Determination and Control
(1978) · Astrophysics and Space Science Library · doi:10.1007/978-94-009-9907-7 · Cited by 1,892
Cited by
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
Filippeschi, A., Schmitz, N., Miezal, M., Bleser, G., Ruffaldi, E., & Stricker, D. (2017) · Sensors · doi:10.3390/s17061257 · Cited by 361
Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing
Sabatini, A. M. (2011) · Sensors · doi:10.3390/s110201489 · Cited by 349
Kalman filtering for relative spacecraft attitude and position estimation
Kim, S.-G., Crassidis, J. L., Cheng, Y., Fosbury, A. M., & Junkins, J. L. (2007) · Journal of Guidance Control and Dynamics · doi:10.2514/1.22377 · Cited by 320
Pasqualetto Cassinis, L., Fonod, R., & Gill, E. (2019) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2019.05.008 · Cited by 156
Extended Kalman Filter for Spacecraft Pose Estimation Using Dual Quaternions
Filipe, N., Kontitsis, M., & Tsiotras, P. (2015) · Journal of Guidance, Control, and Dynamics · doi:10.2514/1.g000977 · Cited by 103
Metadata
- Authors
- Lefferts, E. J., Markley, F. L., & Shuster, M. D.
- Venue
- Journal of Guidance, Control, and Dynamics
- Published
- 1982-09
- Volume
- 5
- Issue
- 5
- Pages
- 417-429
- ISSN
- 0731-5090 (ISSN-L)
- 1533-3884
- OpenAlex
- W2117102490
- Cited by
- 1,344
- References
- 2
- FWCI
- 12.86
- 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 Spacecraft Attitude Estimation. https://app.cochat.ai/papers/10.2514/3.56190
- The paper
- Lefferts, E. J., Markley, F. L., & Shuster, M. D. (1982). Kalman Filtering for Spacecraft Attitude Estimation. Journal of Guidance, Control, and Dynamics. https://doi.org/10.2514/3.56190
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.