** 1927 Golden Dawn, music Emmerich Kalman and Stothart

** Interference filter

** Dichroic filter

** Electronic filter, an electronic circuit which processes signals, for example to remove unwanted frequency components

** Digital filter, a system that performs mathematical operations to reduce or enhance certain aspects of a signal

** Analogue filter, a basic building block of signal processing much used in electronics, see Passive analogue filter development

** Email filter

** Internet filter

** Affective filter, an impediment to learning or acquisition caused by negative emotional (" affective ") responses to one's environment.

** Cabin pollen filter added.

** DSL filter, also known as a POTS filter

** Particle filter

** QuarkXPress 6. 1 ( 2004 )-First version with Excel Import filter.

** Jive filter, an English-to -" Jive " automatic translation program

** nops: filter out redundant modifies

** Subtractive synthesis, a method of creating a sound by removing harmonics, characterised by the application of an audio filter to an audio signal

** Pass band or Passband, the range of frequencies that can pass through an electronic filter without being attenuated

** SAW filter, a type of electronic filter

** In the case of narrow-band bandpass filters, the Q determines the-3dB bandwidth but also the degree of rejection of frequencies far removed from the center frequency ; if these two requirements are in conflict then a staggered-tuning bandpass filter may be needed.

** For high-pass and low-pass ( as well as band-pass filters far from the center frequency ), the required rejection may determine the slope of attenuation needed, and thus the " order " of the filter.

** Chebyshev filter – slight peaking / ripple in the passband before the corner ; Q > 0. 7071 for 2nd-order filters

** Butterworth filter – flattest amplitude response ; Q = 0. 7071 for 2nd-order filters

Examples of algorithms are the Fast Fourier transform ( FFT ), finite impulse response ( FIR ) filter, Infinite impulse response ( IIR ) filter, and adaptive filters such as the Wiener and Kalman filters.

A well used state-space filter is the Kalman filter published by Rudolf Kalman in 1960.

* Kalman filter

* 2008: Rudolf E. Kalman for developing the Kalman filter.

The navigation system then uses a Kalman filter to integrate the always-available sensor data with the accurate but occasionally unavailable position information from the satellite data into a combined position fix.

The Kalman filter tracks the average state of a system as a vector x of length N and covariance as an N-by-N matrix P. The matrix P is always positive semi-definite, and can be decomposed into LL < sup > T </ sup >.

* Kalman filter

* Kalman filter

The Kalman filter keeps track of the estimated state of the system and the variance or uncertainty of the estimate.

The Kalman filter, also known as linear quadratic estimation ( LQE ), is an algorithm which uses a series of measurements observed over time, containing noise ( random variations ) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those that would be based on a single measurement alone.

More formally, the Kalman filter operates recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state.

The Kalman filter has numerous applications in technology.

Furthermore, the Kalman filter is a widely applied concept in time series analysis used in fields such as signal processing and econometrics.

The algorithm works in a two-step process: in the prediction step, the Kalman filter produces estimates of the current state variables, along with their uncertainties.

From a theoretical standpoint, the main assumption of the Kalman filter is that the underlying system is a linear dynamical system and that all error terms and measurements have a Gaussian distribution ( often a multivariate Gaussian distribution ).

Extensions and generalizations to the method have also been developed, such as the Extended Kalman Filter and the Unscented Kalman filter which work on nonlinear systems.

Stanley F. Schmidt is generally credited with developing the first implementation of a Kalman filter.

This Kalman filter was first described and partially developed in technical papers by Swerling ( 1958 ), Kalman ( 1960 ) and Kalman and Bucy ( 1961 ).