Construct · 1 fields · 3 citations

Kalman filter: a standalone construct

One name. One construct. A landmark on the map.

● anchor · a standalone construct Nearest construct by meaningfiltercompare the meanings
How to read this

Every construct on the map is compared with every other by the meaning of its definitions, and the closest one is surfaced here. The nearest construct to Kalman filter is filter.

Hold this as a prompt, not a verdict. Two constructs sitting close in meaning is a reason to read them side by side and decide for yourself whether they are one idea under two names. It is not proof that they are. Compare the meanings in the Collider.

Kalmanfilter1 CONSTRUCTKalman filterKALMAN · STATE ESTIMATE

A single standalone construct; nearby constructs are located against it in the Collider. Tap a field for its definition, unit, and measures.

Signal processing and control

Recursive estimator of a dynamic system's state from noisy measurements

Rudolf E. Kalman's 1960 paper A new approach to linear filtering and prediction problems introduced a recursive algorithm that estimates the hidden state of a linear dynamic system from a stream of noisy measurements, updating its estimate each time a new measurement arrives rather than reprocessing the whole history. The procedure alternates two steps: a prediction that carries the state and its uncertainty forward through a model of the system, and a correction that blends the incoming measurement into the prediction by weighting each source according to its estimated error, a weight known as the Kalman gain. Kalman and Bucy's 1961 companion paper New results in linear filtering and prediction theory extended the same result to continuous time and established its duality with the optimal-control problem, and the method was soon adopted for spacecraft navigation, beginning with the Apollo program. What makes the Kalman filter a landmark is that, for a linear system driven by Gaussian noise, it is the optimal least-squares estimator of the state, and it produces that estimate online with fixed memory and cost, which is why it underlies navigation, target tracking, and signal estimation across engineering.[1][2][3]

Unit
Units of the estimated state, accompanied by an error covariance matrix
Recovery means
Not applicable
Measured by
State estimate and its error covariance, updated recursively from each new measurement via the prediction and correction steps

Next

Run it through the Collider, or take the citations with you.

See how Kalman filter sits against neighbouring constructs in the Collider, or export every reference on this page in one click.

Open in ColliderExport BibTeXReport this entry

Nearby constructs

filter3 fields · 3 citationsjingle · one word, three constructsnoise4 fields · 5 citationsjingle · one word, four constructsstate4 fields · 6 citationsjingle · one word, four constructs

References

  1. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45. https://doi.org/10.1115/1.3662552
  2. Kalman, R. E., & Bucy, R. S. (1961). New results in linear filtering and prediction theory. Journal of Basic Engineering, 83(1), 95–108. https://doi.org/10.1115/1.3658902
  3. Anderson, B. D. O., & Moore, J. B. (1979). Optimal filtering. Prentice-Hall.

Cited on this page?

Confirm your construct, or correct us.

If your work is cited behind one of these entries, you know better than we do whether we captured the meaning right. Confirm it, suggest an edit, or dispute it, and your name goes on the entry as an author who has checked it, linked to your ORCID.

Claim your construct