Ok - the extended kalman filter ended up being much like the basic kalman
filter for this situation. The integration offset is still a problem
unfortunately, do anyone know how to avoid this without introducing a
GPS?
Wicked,
thanks.
Reply by Mad Prof●May 8, 20062006-05-08
"elspeth" <v_enusinfurs@yahoo.co.nz> wrote in message
news:25WdnY1nEPRJesDZnZ2dnUVZ_vWdnZ2d@giganews.com...
> Hello,
> I am new to Kalman filters but know the basics fairly well. I have a
> device that will be pulled behind a boat (in the y direction) with several
> accelerometers and a few gyros -
You'd better watch that the EKF doesn't get wet. They don't perform well
when they're wet.
M.P
>Hello,
>I am new to Kalman filters but know the basics fairly well. I have a
>device that will be pulled behind a boat (in the y direction) with several
>accelerometers and a few gyros - I implemented the linear discrete Kalman
>filter but got a terrible integration offset because obviously I have to
>integrate the accelerometers twice and the gyros once with the transition
>matrix A, state vector xk, control matrix B and control vector u:
>
> 1 T 0 0 0 0 0 0 0 x
> 0 1 0 0 0 0 0 0 0 x_dot
> 0 0 1 T 0 0 0 0 0 y
>A = 0 0 0 1 0 0 0 0 0 xk = y_dot
> 0 0 0 0 1 T 0 0 0 z
> 0 0 0 0 0 1 0 0 0 z_dot
> 0 0 0 0 0 0 1 0 0 phi_x
> 0 0 0 0 0 0 0 1 0 phi_y
> 0 0 0 0 0 0 0 0 1 phi_z
>
> 0 0 0 0 0 0 0 0 0 0
> 0 T 0 0 0 0 0 0 0 x_dot_dot
> 0 0 0 0 0 0 0 0 0 0
> 0 0 0 T 0 0 0 0 0 y_dot_dot
>B = 0 0 0 0 0 0 0 0 0 uk = 0
> 0 0 0 0 0 T 0 0 0 z_dot_dot
> 0 0 0 0 0 0 T 0 0 phi_x_dot
> 0 0 0 0 0 0 0 T 0 phi_y_dot
> 0 0 0 0 0 0 0 0 T phi_z_dot
>
>The uk vector is also the measurement vector zk, as the accelerations and
>changes in angle are the sensor outputs I am reading from.
>
>I cannot understand how to change this to conform with the EXTENDED kalman
>filter, as I assume this would eliminate the integration offset. I am not
>yet looking to translate from the free body frame to the navigation frame
>or anything - shouldn't this be simple?!
>
>I am so confused,
>any help would be SO appreciated -
>you Kalman dudes are amazing.
>Ellie
>
>
Hello,
I am new to Kalman filters but know the basics fairly well. I have a
device that will be pulled behind a boat (in the y direction) with several
accelerometers and a few gyros - I implemented the linear discrete Kalman
filter but got a terrible integration offset because obviously I have to
integrate the accelerometers twice and the gyros once with the transition
matrix A, state vector xk, control matrix B and control vector u:
1 T 0 0 0 0 0 0 0 x
0 1 0 0 0 0 0 0 0 x_dot
0 0 1 T 0 0 0 0 0 y
A = 0 0 0 1 0 0 0 0 0 xk = y_dot
0 0 0 0 1 T 0 0 0 z
0 0 0 0 0 1 0 0 0 z_dot
0 0 0 0 0 0 1 0 0 phi_x
0 0 0 0 0 0 0 1 0 phi_y
0 0 0 0 0 0 0 0 1 phi_z
0 0 0 0 0 0 0 0 0 0
0 T 0 0 0 0 0 0 0 x_dot_dot
0 0 0 0 0 0 0 0 0 0
0 0 0 T 0 0 0 0 0 y_dot_dot
B = 0 0 0 0 0 0 0 0 0 uk = 0
0 0 0 0 0 T 0 0 0 z_dot_dot
0 0 0 0 0 0 T 0 0 phi_x_dot
0 0 0 0 0 0 0 T 0 phi_y_dot
0 0 0 0 0 0 0 0 T phi_z_dot
The uk vector is also the measurement vector zk, as the accelerations and
changes in angle are the sensor outputs I am reading from.
I cannot understand how to change this to conform with the EXTENDED kalman
filter, as I assume this would eliminate the integration offset. I am not
yet looking to translate from the free body frame to the navigation frame
or anything - shouldn't this be simple?!
I am so confused,
any help would be SO appreciated -
you Kalman dudes are amazing.
Ellie