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From: Srinath M <srinath3142@gm...>  20100128 18:46:49

Hi Ego Sorry to reply late, i am no expert on actual implementation, and am trying to do the same thing as you. I am not sure if your A matrix is correct Shouldn't the continous system, just be represented as xdot = Acontx +Bcontu + w I assume like you that the state vector is [true_pos true_velocity accelrometer_bias]. Let us regard accelrometer reading as an input u to the system. For this state vector Acont would be [0 1 0; 0 0 1; 0 0 0],Bcont would be [0;1;0] and w would be white noise So for the discrete system, shouldn't A just be Adisc=[1 dt 0;0 1 dt; 0 0 1]. and this doesn't match with your A Pls correct me if i am wrong. I hv learnt KF's from this site. http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html Regards Srinath On Tue, Jan 19, 2010 at 5:45 , ego <egogoitz@...> wrote: > Hello to all, > I'm new here, and already need your help!! > > I'm using a kalman filter to find out the linear position of a moving > object (movement limited to a single axis), where I use a ADX202 > accelerometer to measure the acceleration, and take some noisy measurements > of the position as well. > > I'm adapting the rotomotion code for a tilt sensor, where tilt is measured > with a 2 axis accelerometer and angular rate with a gyro. I guess this code > is well known but I post the link here: > http://scratchpad.wikia.com/wiki/RotomotionCode > > In my project the state vector would be: > > X=[position, velocity, acceleration_bias] > > And so the state functions: > > Xdot=[position_dot, velocity_dot, acceleration_bias_dot] > > position_dot=velocity > velocity_dot= accelerationacceleration_bias_dot > acceleration_bias_dot=0 > > And the covariance matrix (initial state): > > P=[1 0 0] > [0 0 0] > [0 0 1] > > The jacobian of Xdot with respect to the states: > > A=[d(pos_dot)/d(position) d(pos_dot)/d(vel) d(pos_dot)/d(a_bias)] > [d(vel_dot)/d(position) d(vel_dot)/d(vel) d(vel_dot)/d(a_bias)] > [d(a_bias_dot/d(pos) d(a_bias_dot/d(vel) d(a_bias_dot/d(a_bias)] > > A=[0 1 dt] > [0 0 1] > [0 0 0] (is this correct??) > > Pdot=A*P+P'*A+Q > > where Q=[Q_pos 0 0] > [0 Q_vel 0] > [0 0 Q_accel] > > C matrix is: C=[1 0 0] > > > As I said I measure the position with a noisy sensor and the acceleration > so: > > accel = measured_accel  a_bias > vel = vel + accel*dt > pos= pos+vel*dt+1/2*accel*dt^2 > > Inn=pos_meas  p > > And then I update the elements of the matrix Pdot (Pdot=A*P+P'*A+Q) > > Then compute the covariance matrix P=P+Pdot*dt > > E=CPC'+R > > K=PC'inv(E) > > Then update the covariance matrix: P=PKCP > > And finally update the state estimate X=X+K*Inn > > > > The results I get from this filter are quite good, but there is something I > am not doing right. When the object is stopped, I get the correct position, > velocity is zero, BUT the a_bias is not correct as it gives me a residual > acceleration that is not zero. > pos=correct (stopped) > vel=0 > accel=(a_measa_bias)<>0 > > I don't understand exactly how this kalman filter is implemented (and I > cannot find any info on this way of implementing the filter, I mean updating > the covariace matrix P=P+Pdot*dt). > > Can you help me? Do you see anything that I do wrong? > > Thanks for your help > > >  > Throughout its 18year history, RSA Conference consistently attracts the > world's best and brightest in the field, creating opportunities for > Conference > attendees to learn about information security's most important issues > through > interactions with peers, luminaries and emerging and established companies. > http://p.sf.net/sfu/rsaconfdev2dev > _______________________________________________ > http://autopilot.sourceforge.net/ Development mailing list > https://lists.sourceforge.net/lists/listinfo/autopilotdevel > > 
From: Hugo Marien <h.marien@te...>  20100119 17:26:34

Hi, There are only a few readers left here... Here is a bug report that was posted on this forum on 7/22/2008 by Achim Walther. Maybe it can be of some help to you...  I know there's not much going on in this list and that is still understatement, but I want to report a bug in the autopilot code to the few remaining readers. (For amusement or whatever ;) I own a Rotomotion 6DOF IMU. To use it with a Multiplex EasyStar I had to reduce the size of the main board by applying SMD components. I'm using the IMU only for data gathering and doing the maths with a Gumstix computer. Therefore I had to port the Kalman filtering code. I chose the code from ahrs.c that comes in directory autopilot2.5/onboard/avr. Which may be a mistake because it now looks like an abandoned version branch to me. However, line 156 in function kalman_update tries to invert matrix E by saying m33_inv( E, T2 ); but does the opposite. The correct function call would be m33_inv( T2, E ); since E is the source and T2 is the target. Having fixed that, my 3D visualisation of the Kalman filter works fine and I hope it will do the same in the airborne software. But  as I said  I'm afraid of having used an old branch... Regards, Achim.  Regards and success, Hugo I know there's not much going on in this list and that is still understatement, but I want to report a bug in the autopilot code to the few remaining readers. (For amusement or whatever ;) I own a Rotomotion 6DOF IMU. To use it with a Multiplex EasyStar I had to reduce the size of the main board by applying SMD components. I'm using the IMU only for data gathering and doing the maths with a Gumstix computer. Therefore I had to port the Kalman filtering code. I chose the code from ahrs.c that comes in directory autopilot2.5/onboard/avr. Which may be a mistake because it now looks like an abandoned version branch to me. However, line 156 in function kalman_update tries to invert matrix E by saying m33_inv( E, T2 ); but does the opposite. The correct function call would be m33_inv( T2, E ); since E is the source and T2 is the target. Having fixed that, my 3D visualisation of the Kalman filter works fine and I hope it will do the same in the airborne software. But  as I said  I'm afraid of having used an old branch... Regards, Achim. ego wrote: > Hello to all, > I'm new here, and already need your help!! > > I'm using a kalman filter to find out the linear position of a moving > object (movement limited to a single axis), where I use a ADX202 > accelerometer to measure the acceleration, and take some noisy > measurements of the position as well. > > I'm adapting the rotomotion code for a tilt sensor, where tilt is > measured with a 2 axis accelerometer and angular rate with a gyro. I > guess this code is well known but I post the link here: > http://scratchpad.wikia.com/wiki/RotomotionCode > > In my project the state vector would be: > > X=[position, velocity, acceleration_bias] > > And so the state functions: > > Xdot=[position_dot, velocity_dot, acceleration_bias_dot] > > position_dot=velocity > velocity_dot= accelerationacceleration_bias_dot > acceleration_bias_dot=0 > > And the covariance matrix (initial state): > > P=[1 0 0] > [0 0 0] > [0 0 1] > > The jacobian of Xdot with respect to the states: > > A=[d(pos_dot)/d(position) d(pos_dot)/d(vel) d(pos_dot)/d(a_bias)] > [d(vel_dot)/d(position) d(vel_dot)/d(vel) d(vel_dot)/d(a_bias)] > [d(a_bias_dot/d(pos) d(a_bias_dot/d(vel) d(a_bias_dot/d(a_bias)] > > A=[0 1 dt] > [0 0 1] > [0 0 0] (is this correct??) > > Pdot=A*P+P'*A+Q > > where Q=[Q_pos 0 0] > [0 Q_vel 0] > [0 0 Q_accel] > > C matrix is: C=[1 0 0] > > > As I said I measure the position with a noisy sensor and the > acceleration so: > > accel = measured_accel  a_bias > vel = vel + accel*dt > pos= pos+vel*dt+1/2*accel*dt^2 > > Inn=pos_meas  p > > And then I update the elements of the matrix Pdot (Pdot=A*P+P'*A+Q) > > Then compute the covariance matrix P=P+Pdot*dt > > E=CPC'+R > > K=PC'inv(E) > > Then update the covariance matrix: P=PKCP > > And finally update the state estimate X=X+K*Inn > > > > The results I get from this filter are quite good, but there is > something I am not doing right. When the object is stopped, I get the > correct position, velocity is zero, BUT the a_bias is not correct as > it gives me a residual acceleration that is not zero. > pos=correct (stopped) > vel=0 > accel=(a_measa_bias)<>0 > > I don't understand exactly how this kalman filter is implemented (and > I cannot find any info on this way of implementing the filter, I mean > updating the covariace matrix P=P+Pdot*dt). > > Can you help me? Do you see anything that I do wrong? > > Thanks for your help >  > >  > Throughout its 18year history, RSA Conference consistently attracts the > world's best and brightest in the field, creating opportunities for Conference > attendees to learn about information security's most important issues through > interactions with peers, luminaries and emerging and established companies. > http://p.sf.net/sfu/rsaconfdev2dev >  > > _______________________________________________ > http://autopilot.sourceforge.net/ Development mailing list > https://lists.sourceforge.net/lists/listinfo/autopilotdevel > 
From: ego <egogoitz@gm...>  20100119 13:17:09

Hello to all, I'm new here, and already need your help!! I'm using a kalman filter to find out the linear position of a moving object (movement limited to a single axis), where I use a ADX202 accelerometer to measure the acceleration, and take some noisy measurements of the position as well. I'm adapting the rotomotion code for a tilt sensor, where tilt is measured with a 2 axis accelerometer and angular rate with a gyro. I guess this code is well known but I post the link here: http://scratchpad.wikia.com/wiki/RotomotionCode In my project the state vector would be: X=[position, velocity, acceleration_bias] And so the state functions: Xdot=[position_dot, velocity_dot, acceleration_bias_dot] position_dot=velocity velocity_dot= accelerationacceleration_bias_dot acceleration_bias_dot=0 And the covariance matrix (initial state): P=[1 0 0] [0 0 0] [0 0 1] The jacobian of Xdot with respect to the states: A=[d(pos_dot)/d(position) d(pos_dot)/d(vel) d(pos_dot)/d(a_bias)] [d(vel_dot)/d(position) d(vel_dot)/d(vel) d(vel_dot)/d(a_bias)] [d(a_bias_dot/d(pos) d(a_bias_dot/d(vel) d(a_bias_dot/d(a_bias)] A=[0 1 dt] [0 0 1] [0 0 0] (is this correct??) Pdot=A*P+P'*A+Q where Q=[Q_pos 0 0] [0 Q_vel 0] [0 0 Q_accel] C matrix is: C=[1 0 0] As I said I measure the position with a noisy sensor and the acceleration so: accel = measured_accel  a_bias vel = vel + accel*dt pos= pos+vel*dt+1/2*accel*dt^2 Inn=pos_meas  p And then I update the elements of the matrix Pdot (Pdot=A*P+P'*A+Q) Then compute the covariance matrix P=P+Pdot*dt E=CPC'+R K=PC'inv(E) Then update the covariance matrix: P=PKCP And finally update the state estimate X=X+K*Inn The results I get from this filter are quite good, but there is something I am not doing right. When the object is stopped, I get the correct position, velocity is zero, BUT the a_bias is not correct as it gives me a residual acceleration that is not zero. pos=correct (stopped) vel=0 accel=(a_measa_bias)<>0 I don't understand exactly how this kalman filter is implemented (and I cannot find any info on this way of implementing the filter, I mean updating the covariace matrix P=P+Pdot*dt). Can you help me? Do you see anything that I do wrong? Thanks for your help 
From: <sgarrido@go...>  20100102 11:52:37

Ausente por vacaciones hasta el 4 de Enero Contactar con comercial@... I have left for christmas vacation/holiday and will be back again on 4th January. Please contact comercial@... Muchas gracias / Thank you Sergio Garrido 
From: jH<umt@t...>  20100102 11:51:56

<span style="fontfamily: arial,helvetica,sansserif; color: #000000;">Happy New Year to all members of the autopilot list!<br /><br />Thanks for your continued input! Currently my favourite solution is this one:<br /><br />http://www.rls.si/default.asp?prod=am8192b<br /><br />I must admit that I am a bit reluctant to use optical means since I feel that the <br />processing might be much harder. It will probably summer (or winter again.?.;) <br />until I have my test rig up and running so bear with me folks.<br /><br />So long,<br /><br /><br />Juergen<br /><br /><br /><br /> <br /> OriginalNachricht<br /> Subject: Re: [Autopilot] Rotor twist angle<br /> Date: Thu, 31 Dec 2009 20:04:53 +0100<br /> From: Tom Becker <GTBecker@...><br /> To: "jHumt@..." <jHumt@...>, Development and design discussion on the UAV <autopilotdevel@...><br /> <br /> Another optical method:<br /> <br /> Mount two  or several  chip LEDs on the tips at strategic locations. <br /> Run small wiring for power from a battery and currentlimiting <br /> resistance at the hub through the blade or along the leading edge. Run <br /> the blade in darkness and photograph the resulting trace with a long <br /> shutter and pop a single flash to illuminate an optional scale. <br /> Appropriate camera placement and measurements should yield your data  <br /> and provide intuitive interpretation of oddities like flex, <br /> oscillations, etc.<br /> <br /> Green is most visible to the eye, but red woks best for silicon image <br /> sensors. Chip LEDs can often be found on discarded electronics.<br /> <br /> Tom<br /> <br /> <br /> This SF.Net email is sponsored by the Verizon Developer Community<br /> Take advantage of Verizon's bestinclass app development support<br /> A streamlined, 14 day to market process makes app distribution fast and easy<br /> Join now and get one step closer to millions of Verizon customers<br /> http://p.sf.net/sfu/verizondev2dev <br /> _______________________________________________<br /> http://autopilot.sourceforge.net/ Development mailing list<br /> https://lists.sourceforge.net/lists/listinfo/autopilotdevel<br /> </span> 