version 1.2.0.0 (2.5 KB) by
Yi Cao

An implementation of Unscented Kalman Filter for nonlinear state estimation.

Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system.

The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF.

For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and also to simplify the assumption on nonlinear maps.

The code is heavily commented with an example to use the function. Hence, it is sutiable for beginners to learn the UKF. For comparison, the EKF code can be found from http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18189&objectType=FILE

Yi Cao (2021). Learning the Unscented Kalman Filter (https://www.mathworks.com/matlabcentral/fileexchange/18217-learning-the-unscented-kalman-filter), MATLAB Central File Exchange. Retrieved .

Created with
R2007a

Compatible with any release

**Inspired by:**
Learning the Kalman Filter, Learning the Extended Kalman Filter

**Inspired:**
Neural Network training using the Unscented Kalman Filter, Nonlinear least square optimization through parameter estimation using the Unscented Kalman Filter

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MNRNIRuochen Liujianguo weiZygfryd Wieszokpedro martinsExcelente código

Duy Nanisylvere pagnaHello everyone, i am trying to use your code on kalman filter to reduce a noise on the rssi measurement for further analysis where:

X=[ RSSI(k) d(k)] : the state vector at time k

G(k)=[(10*nlog10*d_k+u_k)/u_k 1]' : the iput matrix

F_k= [1 0; (-10*log10*d_k)/d_k 1]' : state matrix

v(k): the process noise

z(k): theoutput vector at time k

H(k): the measurement matrix at time k

P(k): the state covariance matrix at time k

based on your kalman filter could someone please help me to implement it?

thank you.

you can contact me to this email: pagnadanny@yahoo.fr

YUTI KUOArefeh Amrollahi BiyookiTiago SilvaThank you for sharing the implementation. I had a hard time interpreting the algorithm presented in the paper 'The Square-Root Unscented Kalman Filter For State and Parameter-Estimation'. I'm using a square root continuous-discrete version of the UKF and comparing it with the EKF, so I used the measurement update step. If you're using this be sure to use the square root of the measurement noise R, since we are working with the square-root implementation. I used chol(R,'lower') instead of R and the performance became on-par with the EKF.

GTADear Prof. Yi Cao,

How to perform parameter estimation with this implementation? I know you've put two links in the answers below to answer a question similar to this, but you really can not understand. Could you explain it in more detail? Thank you

Denis-Gabriel CapraceNeat implementation of the regular UKF.

KennyOk... figured it out. So what you have to do is ---- open up the ukf.m file, and then look for the word 'example', and then use your mouse COPY the text starting from "n=3; %number of state" all the way through to "end".

And then you PASTE that text into any abitrary Matlab .m file, such as 'file1.m', and then save it. You then put this new file 'file1.m' in the same directory as ukf.m

And then you load 'file1.m' into Matlab, and then run it by hitting the 'run' button.

KennyOk. I can see there are functions included here, and that's about it.

Are there any details that explain what we have to do?

So it's not just a matter of loading the .m file into Matlab and hitting the 'run' button, right?

Dean CarhounI'm looking for a lucid (understandable) implementation of a quaternion-based UKF for 6 DOF gyro/accelerometer Attitude estimation (pitch and roll). Any advice?

olbondRaola raMattias WallinHave got the code to run, howerver something I don't really understand is in the computation of the sigma points. In all literature I've seen (for example the main documentation cited in the code, from Julier and Uhlmann) you are supposed to calculate the sigma points with the help of the square root of (n+kappa)*Pxx.

In this code it is only done as the square root of the constant, not the covariance matrix? Am I missing something?

Zhiwen ChenMark SpillerEstimating the frequency of a cosine works for me. Perhaps try to set the initialization value close to the real value.

Nathan ZimmermanUnless i'm missing something, this code does not work for parameter estimation?

My state transition is simply the identity matrix while my my measurement matrix would be nonlinear.

Trying to identify a cos wave of unknown frequency & amplitude. This is easy with EKF but I can't figure out how w/ the UKF.

xue leeCu Loasaf eilamhi there,

I'm new with kalman filtering and need your help. I'm using the ukf script for solving the next problem: my measurement record the next function h(t) = x_{3}^2/((X_{1}(t)-X0)^2+X_{2}^2).X_{1,2,3}are my unknowns and X0 in known. my state is [X_{1} X_{2} X_{3}]. X_{1} is periodic signal and X_{2} and X_{3} are parameters that are not change but should be estimated. the algorithm is not even close to give a result! can someone help with this.

Yang Luoit seems good

Fabian Gomez BarreroI'm trying to run the code and I'm having the following 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 crash MATLAB and/or

your computer.

Error in ukf>create@(x)[x(2);x(3);(0.5*x(1)*(x(2)+x(3)))]

Not very good with MATLAB here, how can I fix this! Thanks!

iyad raf3wang qqJeremyMatlab's rounding errors were causing my covariance matrix (P) to become non positive definite. However, I have found a solution.

The function nearestSPD was able to correct for errors in P very nicely.

http://www.mathworks.com/matlabcentral/fileexchange/42885-nearestspd

Only thing to watch out for is that nearestSPD can convert any matrix into a symmetric positive definite matrix, not just matrices that are close.

Hope this helps others with the same problem!

Hesam KhazrajHi after ruining example its shows me this error :

Error: File: ukf.m Line: 21 Column: 2

The expression to the left of the equals sign is not a valid target for an assignment.

would you please tell me whats wrong ?

alex xueIt's great help for beginners in researching!

zhanghao zhanghaogood

Anik IslamAnik IslamI recently came across this code on the unscented Kalman filter (and it's great!) but I'm wondering if it can work when the state and observation variables are complex valued.

I was under the impression that the only difference between the UKF and the Complex UKF (CUKF) is to replace transpose operations with conjugate/hermitian transpose. However, when I attempt to use this code for a complex valued system, the cholesky factorization fails.

More details regarding my application are here:

https://www.reddit.com/r/ECE/comments/3cmrlo/anyone_familiar_with_unscented_kalman_filtering/

Any advice is appreciated. Thanks!

Mark@ Matthew (Jun 28)

I had the same problem (with P growing exponentially). Like you said: this has to do with the Alpha parameter. It has to do with how the Unscented Transform calculates its transformed mean. In certain cases (I think when measurement covariance is very low, and process covariance is a few orders of magnitude greater), there can be some rounding errors in Matlab, which causing the transformed mean to come up short.

To fix this, I changed the UT function to be like this:

~~~~~~~~~~

function [y,Y,P,Y1] = ut(f,X,Wm,Wc,n,R)

L=size(X,2);

% y=zeros(n,1); % LINE COMMENTED OUT HERE

Y=zeros(n,L);

for k=1:L

Y(:,k)=f(X(:,k));

% y=y+Wm(k)*Y(:,k); % LINE COMMENTED OUT HERE

end

y = mean([Y(:,1)'; mean(Y(:,2:end)')]); % LINE ADDED HERE

Y1=Y-y(:,ones(1,L));

P=Y1*diag(Wc)*Y1'+R;

MatthewI found that for my system, the covariance matrix was growing like crazy (P_k~10^8*P_k-1) and was getting complex.

Try adjusting the alpha parameter. 10e-3 was way too small for my system, and 0.1 was the bare minimum that I could avoid the covariance issues. 0.15 seemed to work best.

in general, alpha is recommended to be between 10e-3 and 1.

HienHi everybody!

I really have not understood this code yet. In my case, I also study on EKF for GPS data that I want to apply EKF to due with noise and missing data in GPS data. I have one GPS data columm with more than 2000 of length. Who could show me how to do it?

Thank you so much for your kinds

JanVery good example!

Ghulam RasoolGhulam RasoolEmmanuel FarhiThis contribution can be used as an optimizer but is not very efficient then.

ChinedumThank you Prof. Yi Cao.

I have a problem at this point:

X=sigma(x,P,c) %sigma points around x

It keeps saying:

"Index exceeds matrix dimensions.

Error in sigma (line 97)

a=varargin{1}; b=varargin{2}; c=varargin{3}; d=varargin{4};"

when i run this instead:

X=sigma(x,P,c,[],1), it says:

"Error using sigma (line 107)

The A matrix must be square"

Please does anyone have a solution to this? I can't seem to get past this point.

Thank you.

GMarkDear Yi Cao,

I have a problem with the correlation matrix of the measurement. It becomes non-positive definite for some parameters.

How can I handle this problem?

I have already tried same matrix validations but they do not work.

I have to calibrate model's parameters with MLE.

Any comment is appreciated

Thanks

G.

zhang bluetharaDear Prof. Yi Cao,

Thank you very much for the posted Matlab code. I tried to modify the process function of this code as I want.

for example:

s=[1;2;3];

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

h=@(x)x(1) ;

But when I run the program it shows an error of computing sigma points. It says that the matrix P shoud me positive definite. How we can modify this UKF program for any kind of defined function?

Thank you.

xiaoDear Yi Cao,

According to the paper'performance evaluation of UKF-based nonlinear filtering',choose:f=@(x)[x(1)+tao*x(2);x(2)-tao*x(1)+tao*(x(1)^2+x(2)^2-1)*x(2)];

h=@(x)x(1),with covariance of the process noise w(k)given as:Q=0.003^2I,the covaiance of the noise v(k) is given by:R=0.001^2I,initial state:x0=[2.3;2.2],P0=I,the true value of the initial state:x=[0.8;0.2]. The paper proof that when given all these,UKF tends to be divergent.However,based on this code,it seems that the estimator is stable.Does it owe to the weights chosen when doing the prediction?

Scott LevinsonDear Dr. Cao,

I am relatively new to Kalman filtering, and I am very happy to have found your Excellent, heavily commented UKF function and example “ beginners”:

http://www.mathworks.com/matlabcentral/fileexchange/18217-learning-the-unscented-kalman-filter

It seems that your nonlinear function “f” in this code - that you use as an example could be modified from

f=@(x)[x(2);x(3);0.05*x(1)*(x(2)+x(3))]; % nonlinear state equations

relatively easily to a nonlinear function that describes different nonlinear or time-varying features, like a battery’s state-of-charge.

I would most grateful if you could direct me to further literature, that might further guide me, e.g.,

• notes from your class that gives more background on your unscented Kalman filter example, above .

• how to generally select

ki=0; %default, tunable

beta=2; %default, tunable

alpha=1e-3; %default, tunable

• Do state variables x(1:3) in your example above represent states of an actual physical process or is x used purely a as a numerical example ?

• I understand that your Matlab function UKF.m, describes a simplified unscented KF with added process noise and measurement noise:

% x_k+1 = f(x_k) + w_k

% z_k = h(x_k) + v_k

Do you have a more general unscented KF, UKF2.m, that that propagates the nonlinear noises, e.g.,:

% x_k+1 = f(x_k,w_k)

% z_k = h(x_k,v_k) ?

Thanks again for hour excellent work!

Scott

Scott Levinsongrgei need help. i have the above system, but i dont know how to fuse the equations into the filter. can anyone help me??

the system model has three states: X, Y, Th. the equations are:

Xk+1 = Xk + cos(Thk)*u*Dt

Yk+1 = Yk + sin(Thk)*u*Dt

Thk+1 = Thk + w*Dt

the measurement model is:

X1=Xk+1+r*cos(Thk+1 - 90); //r is a constant

Y1=Yk+1+r*sin(Thk+1 - 90);

X2=Xk+1+m*cos(Thk+1); //m is a constant

Y2=Yk+1+m*sin(Thk+1);

X3=Xk+r*cos(Thk+1 + 90);

Y3=Yk+r*sin(Thk+1 + 90);

H1=H2=H3=0;

P1=(-a*X1-b*Y1-c*H1-d) / (a*(XL-X1)+b*(YL-Y1)+c*(ZL-H1)); // a, b, c, d, constants

P2=(-a*X2-b*Y2-c*H2-d) / (a*(XL-X2)+b*(YL-Y2)+c*(ZL-H2));

P3=(-a*X3-b*Y3-c*H3-d) / (a*(XL-X3)+b*(YL-Y3)+c*(ZL-H3));

A1[0] = XL-X1; // XL, YL, ZL, constants

A1[1] = YL-Y1;

A1[2] = ZL-H1;

A2[0] = XL-X2;

A2[1] = YL-Y2;

A2[2] = ZL-H2;

A3[0] = XL-X3;

A3[1] = YL-Y3;

A3[2] = ZL-H3;

Z1[0] = A1[0]*P1 + X1;

Z1[1] = A1[1]*P1 + Y1;

Z1[2] = A1[2]*P1 + H1;

Z2[0] = A2[0]*P2 + X2;

Z2[1] = A2[1]*P2 + Y2;

Z2[2] = A2[2]*P2 + H2;

Z3[0] = A3[0]*P3 + X3;

Z3[1] = A3[1]*P3 + Y3;

Z3[2] = A3[2]*P3 + H3;

Z[0] = Z1[0] + Z3[0]; // Z[i], are the measurements needed for the UKF!!!

Z[1] = Z1[1] + Z3[1];

Z[2] = atan((Z2[1]-Z[1])/(Z2[0]-Z[0]));

please can you help me implement the UKF with these equations?

Xile KangGreat works!

Sally ThompsonTake note of the point made by Haijun Shen if you are planning on using this filter as a basis for an augmented system (where the noise is part of the state vector). Not including the process noise in the function "f" will cause significant bias in the filter results if your noise is not additive or your state vector is augmented.

Otherwise, thanks so much for a great way to learn about unscented filtering!

xiaogangwhat does the x(1),x(2),x(3) represent

f=@(x)[x(2);x(3);0.05*x(1)*(x(2)+x(3))]; % nonlinear state,I don't konw how does the f function come out?

SouravPlease send your comments to majordavuramus@gmail.com..as I am not very frequent visitor.Thanks again

YvonneI have implemented a highly non-linear aircraft tracking app with both an EKF and your UKF. The initial state and state error covariance matrices are the identical as are the observation and process errors. However, I get a decent result with the EKF, but NOT with your UKF... it should be the reverse... Any suggestion?

miftahuddin miftahuddinhi Dr.Cao

i

have some problem with my dynamic model. would you help me to apply my model in your "UKF".

could i get your email addres.

Best,

Miftahuddin

student

Sepuluh Nopember Institute of technology

Surabaya

Indonesia

Paul MellorI have an input function also ("u"). How can this be added to the UKF code? Do i simply pass "u" through to the fstate() function via the ut() function?

Yi CaoHi Jordan,

Thanks for the comments. A reference has been added to the updated code. It should be available within a few days.

Yi

Jordan MalofDr. Cao,

I really appreciate your submission, it was a great help. I would only suggest listing a reference or two in your m-file, e.g. (R van der Merwe and EA Wan, 2002). I didn't know about the square-root implementation of the ukf and was, just at first, a bit confused about your implementation. Otherwise everything was very clear and helpfull.

Thanks again!

Jordan

Big AndyHaijun ShenHi Yi,

In propagating the process by

[x1,X1,P1,X2]=ut(fstate,X,Wm,Wc,L,Q);

You include Q in the covariance P1, but the propagated states in matrix X1 does not include any process noise because you are assuming additive noise and your f function does not account for process noise. In turn, when you feed X1 into

[z1,Z1,P2,Z2]=ut(hmeas,X1,Wm,Wc,m,R);

to get the measurement matrix Z1, Z1 does not include the effect of any process noise. Therefore, when you use Z1 and z1 to calculate P2, even though you add R onto P2, P2 is not a true representation of Pyy. In your example, Pyy is off by Q.

In linear terms, your X1 consists of Ak*xkhat instead of Ak*xkhat+wk even though your P1 is Ak*Pkhat*Ak'+Qk. And your P2 is C_{k+1}*Ak*Pkhat*AK'*C_{k+1}' + R. The correct P2 should be C_{k+1}*(Ak*Pkhat*AK'+Q)*C_{k+1}' + R.

By the way, I think the augmented version is still applicable to cases with additive noises, although one may choose not to use it because of added complexity.

Looking forward to your opinion.

Thanks.

Haijun Shen

Yi CaoHi holland,

Yes, we can. Please check the following two FEX entries for details.

Yi

http://www.mathworks.com/matlabcentral/fileexchange/18356

http://www.mathworks.com/matlabcentral/fileexchange/18355

hollandHi Dr Cao

why we can not use this UKF algorithm for parameter and state estimation both just like EKF algorithm. What needs to be done to play with this UKF algorithm for state-parameter estimation.

It seems this UKF algorithm is useless and much touted advantage over EKF is not true.

GuapaHi Yi Cao,

i still can't turn the program, please can you tell me how may i do it, since the dowloading of the file to the right execution. matlab always returns errors

tim HeightsHello Dr Yi Cao

are you planing to post square root UKF for parameter estimation?

Gauri PatilHi,

Is it possible to use the UFK when the non-linear function 'f' is unknown. But instead there is a 'map' (non deterministic) which is known. I want to filter the measurement signal using this non-deterministic 'map' which is only a set of samples and is seen periodically in the measurement signal.

Thanks.

Peter Schenkelok for some reasion my previous posting got lost , so once again :

I am using UKF to estimate distances from radio signal strength. Eventhough the RSSI error (measurement equation) is gauss distributed UKF performs very poorly and I cannot understand why as it seems the perfect choice for this kind of problem. I set the measurment nois to the std I got from the training data. My system equations are

f=@(x)[abs(x(1)+x(2));abs(x(3)-x(1));x(1)] ;

h=@(x)[-log10(x(1))*10*pl-A];

for f :

x1: new distance = old distance + velocity

x2: velocity = difference(old distance, new distance)

x3: old distance

h is simply a given transformation from distance to radio singal strength

I cannot find any reason for the poor performance as it should be the best filter for this kind of application. Am I missing some important issues ?

Peter Schenkelsorry...ekf should be ukf in the previous posting

Yi CaoPeter,

This means the iteration of ukf is unstable. You have to adjust P, Q, etc to make it stable.

Yi

Peter SchenkelHi Yi Cao,

First of all, thanks for your contribution here. I do have a question though, I do get for some parameter combinations a complex covariance matrix, the parameters look like this :

z = -78

c = 1.7321e-004

P =

1.2500 + 0.0000i 0.0000 - 0.0000i 0.0000 - 0.0000i

0.0000 + 0.0000i 0.4438 + 0.0000i 0.0000 + 0.0000i

0.0000 + 0.0000i 0.0000 - 0.0000i 1.2500 + 0.0000i

x =

0.5807 - 0.0000i

-5.5018 + 3.7078i

0.7954 - 0.0000i

Then I get this error :

??? Error using ==> chol

Matrix must be positive definite with real diagonal.

I assume that this is due to the complex covariance matrix.

I have no idea how this matrix can become complex as in my oppinion the only way it can become complex is if c would be negative which it isn't here...

Additionally, I would like to measure distances using radio signal strength, therefore I have actually the distances from RSSI values and additional velocity from the last step to the current step, is it possible to process these information with this implementation as well ?

Thanks for any help!

Best

Peter

Yi CaoRight. However, K=P12*inv(P2). Hence, K*P2 = P12.

This leads to K*P2*K' = P12*K'. Therefore, P=P1-P12*K'.

HTH

Yi

Erik RobersI think the covariance updat should be:

P=P1-K*P2*K'

Erik

Yi CaoIt seems that your model is not stable. You may wish to adjust P, Q and R matrices to see if this helps.

Yi

Dapat Chawahhello Dr.Yi

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?

V. PoorNan LuoHi,Dr.Cao

The same question as Adam.

Thank you

Ramya Gangishettywell i'm doing my research project and the topic is comparison of EKF and UKF in non-linear state estimation. The non-linear model which i have used gives correct results for EKF but i'm not able to get the correct results for UKF. I don't know what the problem is. I was wondering if you could look at my model and suggest a solution to it.

fengtset tsaiThe same question as Loki,

if the measurement equation is nonlinear in state variable, the estimated state variable does not change with actural (simulated) state variable. Any suggestion?

fengtset tsaiHi, Dr. Cao,

Is the covariance update correct?

P=P1-K*P12'; %covariance update

Some paper wrote:

P=P1-K*P12*K';

fengtset

Adam AttarianGreetings,

While I understand it is no longer necessary to augment the states when you consider additive noise, it is also apparent that you then only have to use the first L weights, and not the 2L+1 weights. Can you comment on this? Is anything lost or gained by using L weights or 2L+1 weights in the additive noise case?

I ask only because I saw degraded performance when I switched from using all 2L+1 weights to using only the first L weights in my program (I am not augmenting the states).

Thanks,

Adam

NC State Univ

? ?????????????

Yi CaoThere are a few different versions of UKF. I used unaugmented one, where Q and R have to be explicitly used. There are some augmented versions, where Q and R are included in P.

hth

Yi

ben ouissemI'm student in france and i have seen your program about UKF (unscented kalman filter) in the page : http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18217&objectType=file . I don't understand why the function UKF nead the covariance R and Q coz in the algorithm UKF we can find in the paper : http://mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2001-BMVC-Stenger-kalman.pdf (page 4) the UKF just need the covariance P and the state x.

Sorry for my english if it was difficult to understand my question.

Can you please help to understand the UKF.

Thank you

Yi CaoHi, Kavin

Thanks for comments. chol is more efficient and robust than sqrtm.

kavin trivedihey Yi cao,why do u use chol function instead of sqrt ,what is the advantage of doing so,pls clarify chol function u use.

Yi CaoLoki

States is not evolved by the UKF. You should have another simulation model to evolve states, then send output of the model to UKF to estimate the states. If you send me you model through email I may be able to see what is you problem.

Loki IncHi; i tried your function with this, f=@(x)[-x(2);-exp(-a*x(1))*x(2)^2*x(3);0]; % nonlinear state equations

h=@(x)[sqrt(m^2+(x(1)-Z)^2)]; % measurement equation

s=[3*10^5 2*10^4 1*10^-3]';

but the state xV seems to do not evolve after the first step.

Any suggestion? Did i make something wrong?

Yi CaoDear Atilla Aydogdu,

Thank you for your comments. The augmented state variables are only applable if the process noise and measurement noise are non-additive, i.e. they are in the nonlinear functions:

x(k+1)=f(x(k),u(k),w(k))

y(k+1)=h(x(k+1),u(k+1),v(k+1))

In this case, we have to propagate w and v through the nonlinear functions, hence have to have extra augmented dimensions in state space to evaluate these nonlinear functions. That is why the state space dimention becomes 2L+1.

As I stated in the description of my UKF submission, for tutorial purpose, we only consider a simple case, i.e. the noises are additive, where the equarions are:

x(k+1)=f(x(k),u(k))+w(k)

y(k+1)=h(x(k),u(k))+v(k)

In this case, both w and v are not a part of these nonlinear functions, hence, do not need to propagate through these functions. Hence, we do not need the state space augmentation. We can prove, in this case, the non-augment state space formulation is equivalent to the augmented state space formulation.

The reason to assume additive noises is that normally, we do not know how exactly noises influence a system, hence do not realy know how to represent them in nonlinear functions. In this case, it is sensible to assume noises are additive. In the Julier's paper, since it is an academic article, certainly, it makes sense to discuss a more general case, that is to include noises within these nonlinear functions.

Conclusion: if we know how to represent noises in nonlinear functions, then use augmented formulation. Other wise, we can assume additive noises and use the simplified formulation without the state space augmentation.

hth.

Yi CaoHi, Hao Li

The function "[z1,Z1,P2,Z2]=ut(hmeas,X1,Wm,Wc,m,R)" is the subfunction included in the file from Line 72 to Line 95.

Hi, Terry Zeng,

The line you mentioned is line 69. 'z1' is calculated in line 66:

[z1,Z1,P2,Z2]=ut(hmeas,X1,Wm,Wc,m,R);

i.e. the line mentioned by Hao Li.

HTH.

Terry ZengHi,in your code. you write

'x=x1+K*(z-z1); %state update'

but the procedure calculating 'z1' has not been given.

Hao Liwhere's the function "[z1,Z1,P2,Z2]=ut(hmeas,X1,Wm,Wc,m,R)"

Taher DERBELYi CaoDear Edwin,

This is not a recursive code. Hence, this error should not happen. I believe this is due to the way you run the example. When you selected the example and pressed control-t to uncomment the selection, you must have saved the change so that the ukf function is recursively called. The right way to run the example, after you uncomment the selection, you should not save the change, just right-click to run the selection. To help other users may come with the same error, I modified the example with block-comments. Now, you can select the example, right-click to run the selection without accidently saving the change.

Hope this help.

edwin de Vries??? Maximum recursion limit of 500 reached. Use set(0,'RecursionLimit',N)

to change the limit. Be aware that exceeding your available stack space can

crash MATLAB and/or your computer.

Error in ==> ukf>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