The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Least-squares estimation: from Gauss to Kalman
Abstract
This discussion is directed to least-squares estimation theory, from its inception by Gauss1 to its modern form, as developed by Kalman.2 To aid in furnishing the desired perspective, the contributions and insights provided by Gauss are described and related to developments that have appeared more recently (that is, in the 20th century). In the author's opinion, it is enlightening to consider just how far (or how little) we have advanced since the initial developments and to recognize the truth in the saying that we ``stand on the shoulders of giants.''
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
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
New Results in Linear Filtering and Prediction Theory
Kalman, R. E., & Bucy, R. S. (1961) · Journal of Basic Engineering · doi:10.1115/1.3658902 · Cited by 6,387
Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications
Wiener, N. (1949) · The MIT Press eBooks · doi:10.7551/mitpress/2946.001.0001 · Cited by 4,826
Stochastic Estimation of the Maximum of a Regression Function
Kiefer, J., & Wolfowitz, J. (1952) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729392 · Cited by 2,133
On the identification of variances and adaptive Kalman filtering
Mehra, R. (1970) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1970.1099422 · Cited by 1,415
Kailath, T. (1968) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1968.1099025 · Cited by 742
Filtering for stochastic processes with applications to guidance
Bucy, R. S., & Joseph, P. D. (1968) · Defense Technical Information Center (DTIC) · Cited by 546
Antosiewicz, H. A., & Leondes, C. T. (1966) · Mathematics of Computation · doi:10.2307/2003574 · Cited by 430
001: On an Absolute Criterion for Fitting Frequency Curves.
Fisher, R. A. (1912) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · Cited by 393
Cited by
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
An introduction to multisensor data fusion
Hall, D. L., & Llinas, J. (1997) · Proceedings of the IEEE · doi:10.1109/5.554205 · Cited by 2,629
Fischler, M. A., & Bolles, R. C. (1987) · Readings in Computer Vision · doi:10.1016/b978-0-08-051581-6.50070-2 · Cited by 2,122
SCAAT: incremental tracking with incomplete information
Welch, G., & Bishop, G. (1997) · Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97 · doi:10.1145/258734.258876 · Cited by 271
Autonomous exploration: driven by uncertainty
Whaite, P., & Ferrie, F. P. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.584097 · Cited by 240
Metadata
- Authors
- Sorenson, H. W.
- Venue
- IEEE Spectrum
- Published
- 1970-07
- Volume
- 7
- Issue
- 7
- Pages
- 63-68
- ISSN
- 0018-9235 (ISSN-L)
- OpenAlex
- W2170081783
- Cited by
- 746
- References
- 15
- FWCI
- 9.85
- 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: Least-squares estimation: from Gauss to Kalman. https://app.cochat.ai/papers/10.1109/mspec.1970.5213471
- The paper
- Sorenson, H. W. (1970). Least-squares estimation: from Gauss to Kalman. IEEE Spectrum. https://doi.org/10.1109/mspec.1970.5213471
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.