DSP Blockset

Kalman Adaptive Filter

Compute filter estimates for an input using the Kalman adaptive filter algorithm

Library

Filtering / Adaptive Filters

Description

The Kalman Adaptive Filter block computes the optimal linear minimum mean-square estimate (MMSE) of the FIR filter coefficients using a one-step predictor algorithm. This Kalman filter algorithm is based on the following physical realization of a dynamic system.

The Kalman filter assumes that there are no deterministic changes to the filter taps over time (that is, the transition matrix is identity), and that the only observable output from the system is the filter output with additive noise. The corresponding Kalman filter is expressed in matrix form as

The variables are as follows

Variable	Description
n	The current algorithm iteration
u(n)	The buffered input samples at step n
K(n)	The correlation matrix of the state estimation error
g(n)	The vector of Kalman gains at step n
	The vector of filter-tap estimates at step n
y(n)	The filtered output at step n
e(n)	The estimation error at step n
d(n)	The desired response at step n
QM	The correlation matrix of the measurement noise
QP	The correlation matrix of the process noise

.

The correlation matrices, Q_M and Q_P, are specified in the parameter dialog box by scalar variance terms to be placed along the matrix diagonals, thus ensuring that these matrices are symmetric. The filter algorithm based on this constraint is also known as the random-walk Kalman filter.

The implementation of the algorithm in the block is optimized by exploiting the symmetry of the input covariance matrix K(n). This decreases the total number of computations by a factor of two.

The block icon has port labels corresponding to the inputs and outputs of the Kalman algorithm. Note that inputs to the In and Err ports must be sample-based scalars. The signal at the Out port is a scalar, while the signal at the Taps port is a sample-based vector.

Block Ports	Corresponding Variables
In	u, the scalar input, which is internally buffered into the vector u(n)
Out	y(n), the filtered scalar output
Err	e(n), the scalar estimation error
Taps	, the vector of filter-tap estimates

An optional Adapt input port is added when the Adapt input check box is selected in the dialog box. When this port is enabled, the block continuously adapts the filter coefficients while the Adapt input is nonzero. A zero-valued input to the Adapt port causes the block to stop adapting, and to hold the filter coefficients at their current values until the next nonzero Adapt input.

The FIR filter length parameter specifies the length of the filter that the Kalman algorithm estimates. The Measurement noise variance and the Process noise variance parameters specify the correlation matrices of the measurement and process noise, respectively. The Measurement noise variance must be a scalar, while the Process noise variance can be a vector of values to be placed along the diagonal, or a scalar to be repeated for the diagonal elements.

The Initial value of filter taps specifies the initial value as a vector, or as a scalar to be repeated for all vector elements. The Initial error correlation matrix specifies the initial value K(0), and can be a diagonal matrix, a vector of values to be placed along the diagonal, or a scalar to be repeated for the diagonal elements.

Dialog Box

FIR filter length

The length of the FIR filter.

Measurement noise variance

The value to appear along the diagonal of the measurement noise correlation matrix. Tunable.

Process noise variance

The value to appear along the diagonal of the process noise correlation matrix. Tunable.

Initial value of filter taps

The initial FIR filter coefficients.

Initial error correlation matrix

The initial value of the error correlation matrix.

Adapt input

Enables the Adapt port.

References

Haykin, S. Adaptive Filter Theory. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1996.

Supported Data Types

• Double-precision floating point
• Single-precision floating point

To learn how to convert your data types to the above data types in MATLAB and Simulink, see Supported Data Types and How to Convert to Them.

See Also

LMS Adaptive Filter	DSP Blockset
RLS Adaptive Filter	DSP Blockset

See "Adaptive Filters for related information.

Interpolation

LDL Factorization