Peter Schenkel

ok 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 ?

Hi 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