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### Highlights fromLearning the Extended Kalman Filter

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# Learning the Extended Kalman Filter

by Yi Cao

02 Jan 2008 (Updated 23 Jan 2008)

An implementation of Extended Kalman Filter for nonlinear state estimation.

### Editor's Notes:

This file was selected as MATLAB Central Pick of the Week

File Information
Description

This is a tutorial on nonlinear extended Kalman filter (EKF). It uses the standard EKF fomulation to achieve nonlinear state estimation. Inside, it uses the complex step Jacobian to linearize the nonlinear dynamic system. The linearized matrices are then used in the Kalman filter calculation.

The complex step differentiation seems improving the EKF performance particularly in accuracy such that the optimization and NN training through the EKF are better than through the UKF (unscented Kalman filter, http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18217&objectType=FILE). Other complex step differentiation tools include the CSD Hessian available at http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18177&objectType=FILE.

Acknowledgements

Learning The Kalman Filter inspired this file.

MATLAB release MATLAB 7.4 (R2007a)
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03 Jun 2013

Hello everybody,
i have more general question about the extended kalman filter usage. what is not clear to me why EKF uses non-linear functions f and h for state prediction and estimate, while in other places the Jacobian of these functions is used.
Why the following is never used?
first calculate the liniarized state and measurements models at previous estimate point using Jacobian. Use the liniearized state transition and measurements matrix everywhere instead of non-linear in this specific iteration.
I would really appreciate your help
Thank you

24 May 2013

Great submission, thanks!
One question though: in the parameter explanation you define inputs x and P as "a priori" state estimate and "a priori" estimated state covariance. In my understanding this is not right, as "a priori" values are only available right after the prediction step of the filter.

So, in my opinion x and P are the "a posteriori" values of the previous time step. The "a priori" values of x and P of the current time step are available after the prediction step of your filter (vals x1 and P in lines 51 and 52).
Do you agree?

28 Mar 2013

ERROR!!!

Input argument "wstate" is undefined.

Error in ==> ekf at 51
[x1,A]=jaccsd(wstate,x); %nonlinear update and linearization at current state

27 Mar 2013

Hi guys, i need some help please. I Use Matlab R2012b to try to run the code/example. I usually copy the whole code,place a new editor,highlight the example,right click,left click 'evaluate selection'(as i don't see any 'run').But on Matlab's command window, it shows the highlighted example and says "Undefined function 'ekf' for input arguments of type 'function_handle'." Please who knows what could be wrong? What could i be doing wrong? Thank you. John

07 Mar 2013

hi,
the states are well estimated by EKF,but if chaging in extended state variable at middle of the simulation EKF converdge always to the initial value one.

06 Mar 2013
13 Feb 2013

Hi,

Just one question:

why is the nonlinear update at line 51 done with out a numerical integration but just making the new x_k=f(x_k-1)?

30 Dec 2012
29 Sep 2012

please someone explain what these lines do from line 51 to line 78

21 Jun 2012

f is the nonlinear differential ecuation to be integrated, thats why the @ so that the ekf funtion can call it, an withing the ekf solve this ecuation to get the first estimation, the h is the measurement equation, also as handle function (@) so that it can be called by the ekf to calculate the kalman gain.

20 Jun 2012

can someone explain what these lines do:

f=@(x)[x(2);x(3);0.05*x(1)*(x(2)+x(3))];
h=@(x)x(1);

Thanks!

17 Apr 2012

what does the graph of 2nd and 3rrd state represent here

11 Apr 2012

19 Mar 2012

fstate in line 51 represents the non-linear state equations, which are function of x
in the example fstate is f, which is in line 26

16 Mar 2012

what is fstate in line 51?

14 Mar 2012

@tehreem
the program is working well
copy the program from line 21 to line 46 and run it, its working

13 Mar 2012

hi, im experiencing an error at line 51 and the program is not running due to that
can u please provide guidance in that regard

24 Jan 2012
27 Dec 2011

run the code as below in command window:

n=3
:
:
end

has an error:
??? Undefined function or method 'ekf' for input arguments of type
'function_handle'.
how to run these codes?

02 Nov 2011

Can you go over the steps to properly run this function please?! I am still getting error that have been mentioned above in some comments, mainly:
??? Maximum recursion limit of 500 reached. Use set(0,'RecursionLimit',N)
to change the limit. Be aware that exceeding your available stack space can

Error in ==> ekf>create@(x)[x(2);x(3);0.05*x(1)*(x(2)+x(3))]

Thanks:)

02 Nov 2011
27 Aug 2011

I downloaded the file and ran it on R2006b. Got the following error. Could someone tell me what I am doing wrong? I guess I have to uncomment a few things and run in some sequence, but unable to figure out what

??? Input argument "fstate" is undefined.

Error in ==> ekf at 51
[x1,A]=jaccsd(fstate,x); onlinear update and linearization at current state

08 Jul 2011

Hi;
I get the error message "??? Undefined function or method 'ekf' for input arguments of type 'function_handle'"

04 Jul 2011

thanks you

05 May 2011

Hi!

Is there possible to use the code in a case where I have different rates of prediction and correction steps. (e.g., could I have 10 predictions before make one correction...). Is it easy to do this in the current version of the code?

16 Apr 2011

Hi! This is a nice code for EKF. I have a question though: In your example if we assume that the value 0.05 is unknown parameter and we want simultaneous state and parameter estimation can we augment the state as with the parameter as:
n=4; %number of state
q=0.1; %std of process
r=0.1; %std of measurement
Q=q^2*eye(n); % covariance of process
% or Q=diag[Q 0]; % if no process noise is included in the parameter
R=r^2; % covariance of measurement
f=@(x)[x(2);x(3);x(4)*x(1)*(x(2)+x(3));x(4)]; % nonlinear state equations
h=@(x)x(1); % measurement equation
s=[0;0;1;0.1]; % initial state
x=s+q*randn(4,1); %initial state % initial state with noise
P = eye(n); % initial state covraiance
N=20; % total dynamic steps
xV = zeros(n,N); %estmate % allocate memory
sV = zeros(n,N); %actual
zV = zeros(1,N);
for k=1:N
z = h(s) + r*randn; % measurments
sV(:,k)= s; % save actual state
zV(k) = z; % save measurment
[x, P] = ekf(f,x,P,h,z,Q,R); % ekf
xV(:,k) = x; % save estimate
s = f(s) + q*randn(3,1); % update process
end;

22 Feb 2011

Maria,

Yes, it is possible. Please look at the submission:

http://www.mathworks.com/matlabcentral/fileexchange/18289

19 Feb 2011

Hi, Is it possible to use your code for parameter identfication?

28 Jan 2011

hi Yi
could you suggest an examplar definition of the function "f" together with initial state "s" for some real life example of a system? this would help me and other inexperienced guys to better understand this example. thank you very much in advance.

11 Aug 2010
25 Jun 2010

.hii..
when i run this program..the following error is displayed in the command window:
"??? Input argument "fstate" is undefined.

Error in ==> ekf at 51
[x1,A]=jaccsd(fstate,x); %nonlinear update and linearization at current state"

Also suggest to me how to implement this code for multi sensor data fusion wherein the input is in the form of signals from n different sensors..as in how do I express my input here in terms of f and h.

23 May 2010

Hi, how should I modify the m-file if I want to change the measurement- and process noise to:
w ~ N(u,Q)
v ~ N(e,R)

20 May 2010
20 May 2010

Congratulations. This is the first EKF library I manged to get working at all.

The example takes measurements go in the s matrix, not the x. Now that is fixed everything is good.

It is a long time since I did Kalman or Matlab. You clever guys underestimate how dumb you need to make your comments to get us newbies started.

06 Apr 2010
02 Mar 2010

Aeimit

Yes, it is possible. For example, see

http://www.mathworks.com/matlabcentral/fileexchange/18286

Yi

01 Dec 2009

Is it possible to do constrained nonlinear optimization with EKF?

11 Aug 2009

hi yi, would like to know if its appropriate to use EKF for forecasting of agricultural yields like fish ,rice etc. i am planning to use EXPAR with EKF for the problem stated above and would you kindly be able to give some of your ideas regarding the same.thankyou, with regards bishal.

20 Jul 2009
08 Jun 2009

i wrote a very simple compound pendulum code, and some how this ekf algorithm does not work for that. only change that i had to do to that example file was change the states to 2 and rest
f=@(x)[x(2);-(g/l)*sin(x(1))];
this should give me sinusoidal waveform but it does not.
can you point out to me what could be wrong.

26 May 2009

if in EKF i have to add state noise compensation, any good example or guidance here. how can i add to the example given by Yi Cao.
secondly, any one who could also recommend some book about it.

23 May 2009
28 Apr 2009

For continuous-time EKF, please look at http://www.mathworks.com/matlabcentral/fileexchange/18485

28 Apr 2009

Hi I am looking for an example where the EKF is applied to a continuous-time non-linear system with non-zero inputs (say measurements are taken at regular time samples through a non-linear (even linear would do) measurement process. I have looked around for this kind of example in the standard texts but haven't found any. Also a good source showing the implementation of the EKF wherein we linearize about a single operating point (as against linearizing about the predicted state every time) would be really helpful! Thanks in advance! Rohit

20 Apr 2009

Sorry, this comment is meant to be in the unscented kalman filter file discussion

20 Apr 2009

This code is working good for N<=150
but when N exceeds this limit, a nonsense happens
Is there any improvement to the code considering this error?

05 Apr 2009
17 Feb 2009

This error occurs because you run the example incorrectly so that ekf calls itself more than 500 times. To run the example, you need copy contents between "%{" and "%}" then past it on matlab command window to execute the example.

It also could be because your MATLAB version is too old to support block comments. If that is the case, you can comment out all line by adding "%" at the begining of each line between "%{" and "%}" to solve the problem.

17 Feb 2009

Hi, I still get the error

??? Maximum recursion limit of 500 reached. Use set(0,'RecursionLimit',N)
to change the limit. Be aware that exceeding your available stack space
can

Error in ==> ekf>create@(x)[x(2);x(3);0.05*x(1)*(x(2)+x(3))]

after following your instructions. How do I correctly run the code?

04 Oct 2008

Beware: function ekf changes the value of the measurement covariance matrix R. It shouldn't be the case. Otherwise the code is nice and efficient.

29 Jul 2008

Nice code. But if there is load disturbance on the state, why the estimate from the EKF almost ignores the load disturbance?

21 Jul 2008

Excellent! Nice use of CHOL instead of INV (as can be seen in two other Kalman-filter codes on the File Exchange). Nice to see good numerics at work.

I see that "K=P12*inv(...)" is commented out; that's perfect. It gives the math behind what the CHOL and backslashes are doing.

16 Jun 2008

it is very good and helpful in my project.

11 Jun 2008

Very helpful for learning Kalman Filter Implementation.

06 May 2008

Nicely made, and very intuitive if one has an idea how a linear Kalman Filter works. However I found that numerically solving the Jacobian is not always the best form of linerisation, especially for simpler cases when an analytic Jacobian can be computed by hand.
In my experiments (with simple non-linear models) an analytic Jacobian usually gave a significant improvement of fit when compared to its numeric counterpart. Maybe you could add an option on how it should be solved

03 May 2008
24 Apr 2008
16 Apr 2008

very well.

14 Apr 2008
24 Jan 2008
23 Jan 2008

Dear Edwin,

As I expected, this error is due to your way to run the example because the error message shows that the error occures at line 19, which is a commented line to begin the example.

To correctly run the example, you can follow the following steps:

1. select the example lines correctly
2. press control-t to uncomment the selection
3. right-click to run the selection
4. click un-do to recover the file. (DO NOT click the save button.)

For you and other users' convenient, I updated the file with block-comment lines for the example. Now, you just need to select and right-click to run the example without change the file. The update will appear a few hours later.

Hope this help.

23 Jan 2008

??? Error: File: ekf.m Line: 19 Column: 10
Expression or statement is incomplete or incorrect.

??? Maximum recursion limit of 500 reached. Use set(0,'RecursionLimit',N)
to change the limit. Be aware that exceeding your available stack space can

Error in ==> ekf>create@(x)[x(2);x(3);0.05*x(1)*(x(2)+x(3))] at 25
f=@(x)[x(2);x(3);0.05*x(1)*(x(2)+x(3))]; % nonlinear state equations

17 Jan 2008
09 Jan 2008

Improve efficiency in inverse calculation

16 Jan 2008

update description

23 Jan 2008

Update example with block-comment lines