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# residual

Residual and residual covariance from state measurement for insEKF

Since R2022a

## Description

example

[residual,residualCovariance] = residual(filter,sensor,measurement,measurementNoise) computes the residual and the residual covariance based on the measurement from the sensor and the measurement covariance.

## Examples

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Create an insAccelerometer sensor object and insGyroscope sensor object.

acc = insAccelerometer;
gyro = insGyroscope;

Construct an insEKF object using the two sensor objects. Specify the angular velocity as [0.1 0.1 0.1] $\mathrm{rad}/\mathit{s}$.

filter = insEKF(acc,gyro);
stateparts(filter,"AngularVelocity",[0.1 0.1 0.1]);

Obtain the residuals for a gyroscope measurement of [0.1 0.2 -0.04] $\mathrm{rad}/\mathit{s}$ with a measurement noise covariance of diag([0.2 0.2 0.2]) ${\left(\mathrm{deg}/\mathit{s}\right)}^{2}$.

[residual,residualCov] = residual(filter,gyro,[0.1 0.2 -0.04],diag([0.2 0.2 0.2]))
residual = 3×1

0
0.1000
-0.1400

residualCov = 3×3

2.2000         0         0
0    2.2000         0
0         0    2.2000

## Input Arguments

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INS filter, specified as an insEKF object.

Inertial sensor, specified as one of these objects used to construct the insEKF filter object:

Measurement from the sensor, specified as an M-element real-valued vector, where M is the dimension of the measurement from the sensor object.

Data Types: single | double

Measurement noise, specified as an M-by-M real-valued positive-definite matrix, an M-element vector of positive values, or a positive scalar. M is the dimension of the measurement from the sensor object. When specified as a vector, the vector expands to the diagonal of an M-by-M diagonal matrix. When specified as a scalar, the value of the property is the product of the scalar and an M-by-M identity matrix.

Data Types: single | double

## Output Arguments

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Measurement residual, returned as an M-element real-valued vector, where M is the dimension of the measurement.

Data Types: single | double

Residual covariance, returned as an M-by-M real-valued positive definite matrix, where M is the dimension of the measurement.

Data Types: single | double

## Version History

Introduced in R2022a