JKalman

java.lang.Object
- jhplot.math.kalman.JKalman

```
public class JKalman
extends Object
```
Kalman filter (state).
The structure JKalman is used to keep Kalman filter state. It is created by constructor function, updated by Predict and Correct functions.
Normally, the structure is used for standard JKalman filter (notation and the formulae below are borrowed from the JKalman tutorial [Welch95]):
```
 x_k=A*x_k-1+B*u_k+w_k
 z_k=Hx_k+v_k,
 
```
where:
```
 x_k (x_k-1) - state of the system at the moment k (k-1)
 z_k - measurement of the system state at the moment k
 u_k - external control applied at the moment k
 w_k and v_k are normally-distributed process and measurement noise, respectively:
 p(w) ~ N(0,Q)
 p(v) ~ N(0,R),
 that is,
 Q - process noise covariance matrix, constant or variable,
 R - measurement noise covariance matrix, constant or variable
 
```
In case of standard JKalman filter, all the matrices: A, B, H, Q and R are initialized once after JKalman structure is allocated via constructor. However, the same structure and the same functions may be used to simulate extended JKalman filter by linearizing extended JKalman filter equation in the current system state neighborhood, in this case A, B, H (and, probably, Q and R) should be updated on every step.

Constructor Summary

Constructors
Constructor and Description
`JKalman(int dynam_params, int measure_params)` Constructor in case of no control.
`JKalman(int dynam_params, int measure_params, int control_params)` The construstor allocates JKalman filter and all its matrices and initializes them somehow.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`Matrix`	`Correct(Matrix measurement)` Adjusts model state.
`Matrix`	`getControl_matrix()` Getter
`Matrix`	`getError_cov_post()` Getter
`Matrix`	`getError_cov_pre()` Getter
`Matrix`	`getGain()` Getter
`Matrix`	`getMeasurement_matrix()` Getter
`Matrix`	`getMeasurement_noise_cov()` Getter
`Matrix`	`getProcess_noise_cov()` Getter
`Matrix`	`getState_post()`
`Matrix`	`getState_pre()` Getter
`Matrix`	`getTransition_matrix()`
`Matrix`	`Predict()` Alias for prediction with no control.
`Matrix`	`Predict(Matrix control)` Estimates subsequent model state.
`void`	`setControl_matrix(Matrix control_matrix)` Setter
`void`	`setError_cov_post(Matrix error_cov_post)` Setter
`void`	`setError_cov_pre(Matrix error_cov_pre)` Setter
`void`	`setGain(Matrix gain)` Setter
`void`	`setMeasurement_matrix(Matrix measurement_matrix)` Setter
`void`	`setMeasurement_noise_cov(Matrix measurement_noise_cov)` Setter
`void`	`setProcess_noise_cov(Matrix process_noise_cov)` Setter
`void`	`setState_post(Matrix state_post)` Setter
`void`	`setState_pre(Matrix state_pre)` Setter
`void`	`setTransition_matrix(Matrix transition_matrix)` Getter

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail
- JKalman
```
public JKalman(int dynam_params,
               int measure_params,
               int control_params)
        throws Exception
```
  The construstor allocates JKalman filter and all its matrices and initializes them somehow.
  
  Parameters:
  
  dynam_params -
  
  measure_params -
  
  control_params -
  
  Throws:
  
  IllegalArgumentException - Kalman filter dimensions exception.
  
  Exception
- JKalman
```
public JKalman(int dynam_params,
               int measure_params)
        throws Exception
```
  Constructor in case of no control.
  
  Parameters:
  
  dynam_params -
  
  measure_params -
  
  Throws:
  
  Exception

Method Detail

Predict
```
public Matrix Predict()
```
Alias for prediction with no control.

Returns:

Predict(no control).

Predict

public Matrix Predict(Matrix control)

Estimates subsequent model state.

The function estimates the subsequent stochastic model state by its current state and stores it at state_pre:

 x'_k=A*x_k+B*u_k
 P'_k=A*P_k-1*A^T + Q,
 where
 x'_k is predicted state (state_pre),
 x_k-1 is corrected state on the previous step (state_post)
     (should be initialized somehow in the beginning, zero vector by default),
 u_k is external control (control parameter),
 P'_k is prior error covariance matrix (error_cov_pre)
 P_k-1 is posteriori error covariance matrix on the previous step (error_cov_post)
     (should be initialized somehow in the beginning, identity matrix by default),

Parameters:: control - Control vector (u_k), should be NULL if there is no external control (control_params=0).
Returns:: The function returns the estimated state.

Correct
```
public Matrix Correct(Matrix measurement)
```
Adjusts model state. The function KalmanCorrect adjusts stochastic model state on the basis of the given measurement of the model state:
```
 K_k=P'_k*H^T*(H*P'_k*H^T+R)^-1
 x_k=x'_k+K_k*(z_k-H*x'_k)
 P_k=(I-K_k*H)*P'_k
 where
 z_k - given measurement (mesurement parameter)
 K_k - JKalman "gain" matrix.
 
```
The function stores adjusted state at state_post and returns it on output.
Parameters:

measurement - Matrix containing the measurement vector.

Returns:

setState_pre

public void setState_pre(Matrix state_pre)

Setter

Parameters:: state_pre -

getState_pre
```
public Matrix getState_pre()
```
Getter

Returns:

setState_post

public void setState_post(Matrix state_post)

Setter

Parameters:: state_post -

getState_post
```
public Matrix getState_post()
```

setTransition_matrix

public void setTransition_matrix(Matrix transition_matrix)

Getter

Parameters:: transition_matrix -

getTransition_matrix
```
public Matrix getTransition_matrix()
```

setControl_matrix

public void setControl_matrix(Matrix control_matrix)

Setter

Parameters:: control_matrix -

getControl_matrix
```
public Matrix getControl_matrix()
```
Getter

Returns:

setMeasurement_matrix

public void setMeasurement_matrix(Matrix measurement_matrix)

Setter

Parameters:: measurement_matrix -

getMeasurement_matrix
```
public Matrix getMeasurement_matrix()
```
Getter

Returns:

setProcess_noise_cov

public void setProcess_noise_cov(Matrix process_noise_cov)

Setter

Parameters:: process_noise_cov -

getProcess_noise_cov
```
public Matrix getProcess_noise_cov()
```
Getter

Returns:

setMeasurement_noise_cov

public void setMeasurement_noise_cov(Matrix measurement_noise_cov)

Setter

Parameters:: measurement_noise_cov -

getMeasurement_noise_cov

public Matrix getMeasurement_noise_cov()

Getter

Returns:

setError_cov_pre

public void setError_cov_pre(Matrix error_cov_pre)

Setter

Parameters:: error_cov_pre -

getError_cov_pre
```
public Matrix getError_cov_pre()
```
Getter

Returns:

setGain
```
public void setGain(Matrix gain)
```
Setter

Parameters:

gain -

getGain
```
public Matrix getGain()
```
Getter

Returns:

setError_cov_post

public void setError_cov_post(Matrix error_cov_post)

Setter

Parameters:: error_cov_post -

getError_cov_post
```
public Matrix getError_cov_post()
```
Getter

Returns:

Class JKalman

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

JKalman

JKalman

Method Detail

Predict

Predict

Correct

setState_pre

getState_pre

setState_post

getState_post

setTransition_matrix

getTransition_matrix

setControl_matrix

getControl_matrix

setMeasurement_matrix

getMeasurement_matrix

setProcess_noise_cov

getProcess_noise_cov

setMeasurement_noise_cov

getMeasurement_noise_cov

setError_cov_pre

getError_cov_pre

setGain

getGain

setError_cov_post

getError_cov_post