Cognitoware.Robotics.dll

Class KalmanSensorModel<Z, X>

System.Object

Cognitoware.Robotics.StateEstimation.KalmanSensorModel<Z, X>

A sensor model based on the linear system Z = C*X + error.

ConditionalProbabilityOf(Z, X)

Creates a new GaussianMoment using GetMean and GetError.

Equals(Object)

Inherited from System.Object

Finalize()

Inherited from System.Object

GetError(Z)

Creates an covariance matrix that describes the Gaussian error around the sensor mean.

GetHashCode()

Inherited from System.Object

GetMean(X)

Creates the expected observation from a specific x.

GetType()

Inherited from System.Object

MemberwiseClone()

Inherited from System.Object

ToString()

Inherited from System.Object

A linear sensor model is an implementation of GaussianSensorModel and RandomConditional.
The expected sensor reading is generated by multiplying the matrix C by the sensor x.
The error is the constant covariance matrix q.
P( Z | X ) = A * X + error.
KalmanSensorModel is used in the KalmanFilter algorithm.
Because of the requirement to interact with Matrix, Z and X must both be of type Vector or inherited from Vector.

public KalmanSensorModel()

public final virtual Double ConditionalProbabilityOf(Z observation, X state)

Creates a new GaussianMoment using GetMean and GetError.

`observation`

- The observation for the error.`x`

- The current x.A new Gaussian distribution.

public final virtual Matrix GetError(Z z)

Creates an covariance matrix that describes the Gaussian error around the sensor mean.
Returns the constant value Q.

`z`

- The observation for the error.A covariance matix that describes the error of the sensor reading.

public final virtual Z GetMean(X x)

Creates the expected observation from a specific x.
Z = C * X.

`x`

- The x at which the observation is made.The expected observation at a x.