Kalman filtering with multiplicative noise

A couple of comments:

1) Unless v1,v2 have covariance then they are independent and you can deal with them separately. It doesn't seem to make any sense to combine the estimation of z1,z2 in one Kalman filter. 2) In the case of equation two, typically v2 would be restricted to be 3) Given the above then defining nz2=log(z2), nx2=log(x2), nv2=log(v2) would yield a perfectly normal distribution; sorry for the pun:) 4) Even is v2 wasn't truly log-normal you could try to use the first and second moments as a description of the real ln(v2) distribution and see how adequate the description works.

IMHO: all of engineering mathematics is descriptive and the only question is how adequate the descriptions are.

Ray

Concerning the uneven sampling there have been discussions of this on this newsgroup in the past; which I haven't followed carefully. In my experience the uneven sampling could be rolled into either multiplicative or additive noise and dealt with as another noise term; but I had enough design freedom so that I could make do with these approximations. That is to say, the signals were always "chopped" to avoid 1/f noise and actual information was contained in small attenuation of the signals; thus the effect of the changing sampling point could be estimated/bounded by the chopped signal independent of the information.

RayR

Ray

As Ray pointed out these seem to be completely independent measurements, unless V1 and V2 are correlated.

With any affine system, such as the log-domain volume system, you can subtract out the offset to make the system linear, do your linear processing, then add the offset back in. I.O.W., calculate log z2 = log x2 + log v2 + 1, then define dlz2 = log z2 - 1, then find your estimated dlz2, then back up to your estimated z2. (Note that your estimate of z2 probably won't be strictly optimal in the mean-squared sense, only your estimate of dlz2 will be. But you'll certainly be closer than you would by a lot of other means).

My state transition for these two measurements is given by:

F =3D [dt 1 1 0]

where dt is the time step, so as far as I understand it these measurements are correlated - if z1 is positive, then z2 will be increasing and vice versa.

Doing this in the log domain makes sense, but how can I have one measurement in the log domain and the other not? The state transition won't make sense.

Equation 2 will actually reach zero quite often, so maybe I need multiplicative and additive noise in my measurement model for z2, not too sure about that.

What do you mean by the eventual n % for nonlinear dynamic system: % x_k+1 = f(x_k) + w_k % z_k = h(x_k) + v_k % where w ~ N(0,Q) meaning w is gaussian noise with covariance Q % v ~ N(0,R) meaning v is gaussian noise with covariance R

so the EKF looks appropriate for non-linear process models and measurement models that can be represented by any arbitrary functions f(x) and h(x), but the noise is still assumed to be additive.

You probably need an extended Kalman filter, where you use your current best guess of the flow to update the covariance. This will be more computationally intensive because you won't be able to pre-calculate your covariance, and it'll be nonlinear because you'll be multiplying by one of your states.

That's about as much as I know about the subject, so try google, or get a copy of "Optimal State Estimation" by Simon and have fun.

Alternately, can you do a good enough job with good ol' low-pass filters?

He means that it's a nonlinear system, and therefore you can't assume that just because it's locally stable around some state that it's globally stable for any state. In particular, there may be input vectors that will drive it into a limit cycle or off toward infinity, either permanently or temporarily.

Contrary to the beliefs of some, the MathWorks isn't on high, and MatLab isn't an extension of the Bible. I would trust them for examples, but don't take their documentation for anything but a means of selling you copies of MatLab.

If this is a student project then why not assume there will be a set point and the controller will maintain that set point with little variation. Now the volume is fixed and the variance is therefore fixed. Now you can assume the system is linear around that set point.

Peter Nachtwey

here:

If you change the second equation to log's, and if the log(v2) was adequately described by a normal distribution then the noise would be additive and the non-linearity would pushed into the x2/z2 and it seems that the EKF could deal with it. In terms of the wikipedia article and your state equation z1=x1+v1 lz2=lx2+lv2

and presumptively x1'=dt*lx1+exp(lx2)+v1 lx2'=log(x1)+lv2

You should really presume these state equations are wrong! Without the differential equations including the noise terms I really can't get a handle on them mentally ( I usually get the first go round wrong anyway).

RayR

I really suggest that you make up a graphical model of the system, Simulink or Scicos ( I can deal with Scicos), both the physical system and then the proposed filtering.

RayR

So your multiplicative noise can be negative? This seems strange.

In any case, why not simply derive the appropriate filtering from scratch instead of trying to find off the shelf solutions? That way you can incorporate all the specifics of your problem.

illywhacker;

here:

The EKF propagates first and second moments which is essentially treating the problem like it can be approximated like a linear Gaussian.

Particle filters come in various flavors and there are versions that can handle multiplicative noise.

The link I posted is a user contribution, not an example from the Mathworks, so I don't trust it entirely. However the Wikipedia article on EKF's seems to show the same formulation with the noise being assumed to be additive, only the process and measurement models are allowed to be non-linear:

Does this mean the EKF is not appropriate for my problem or am I misunderstanding how it works?

I have already implemented a regular Kalman filter simply by approximating both of the noise terms as additive noise and the results are okay but not great (another idea I might try is approximating both of them as multiplicative noise, and then I can do all the calculations in the log domain). To improve it I want to try incorporating the multiplicative noise. When z2 terms have smaller values, we have more confidence in them (less variance), so I want to explicitly incorporate this into the filter. In particular, when z2 reaches zero, we have almost 100% confidence it is correct.

I'm not too familiar with Simulink or Scicos but here's a small diagram:

z2 +---------+ | | ======>| | z1 | |

The 'E' in 'EKF' stands for 'extended'. I'd take that and run with it. If you understand how the Kalman filter works you know how to iterate your covariance matrix with known noise properties -- why not use the predicted volume to set the noise intensity from one iteration to the next?

Good for you -- you're not just using the fancy math because it's there, you're using it because you have to.

(responses to other guy snipped)

Example? Where did this data come from? If it's experimental data, can you post the raw numbers. I can characterize it if you want.

RayR