Re: [Bayes++] Observation model for SIR scheme

[Nicola B. <nb...@es...>]

Steven,

Thanks for your reply. I fixed variance and number of samples also in my model 
and now there are no more exceptions indeed.
As you said, unfortunately the SIR_scheme seems to be more sensitive to the 
model and this might be a big problem for me, since I am tracking humans for 
which a good motion model does not exist in general (although UKF seems to 
perform not too bad).
I read some authors (Arulampalam et al.,  A tutorial on particle filters for 
online nonlinear/non-GaussianBayesian tracking, 2002) suggest Systematic 
Resampling, which is already implemented in your library indeed. What do you 
think?
Also, I was wondering if the SIR_kalman_scheme is basically just a SIR filter 
providing the first two moments, 'x' and 'X', or something more performant (I 
saw for example that it has a different roughening procedure).

Regards,

Nicola


On Saturday 18 Nov 2006 19:20, Michael Stevens wrote:
> Nicola,
>
> On Saturday, 18. November 2006 15:47, Nicola Bellotto wrote:
> > Steven,
> >
> > I modified my model inheriting from Likelihood_observe_model and copying
> > the likelihood function code, but I had continuosly exceptions like
> > "Roughening X not PSD" or "zero cumulative weight sum".
> > So I went back to the simplest example, the PV_SIR.cpp, and modified just
> > a little the observation model multiplying z_pred[0] and Hx(0,0) by 2
> > (see attached file). This caused again a "zero cumulative weight sum"
> > exception. I looked through the library's code but I could not really
> > understand the reason of the problem. What am I doing wrong?
>
> I took a quick look at this because I was fearing a bug. Lucky for me the
> problem is with you small but significant change!
>
> Both the exceptions occur when the the SIR cannot solve the problem. As you
> found this is quite easy to achive.
>
> "zero cumulative weight sum" occurs when the Likelihoods computed for all
> the samples are zero.  When you doubled z_pred and Hx you omited to double
> the true observation at line 199 in PV_SIR.cpp. Because the position is
> 1000 this meant that the observation was 2*1000-1000 away from the true.
> With an position variance of just 1, something 1000 away is very very
> unlikely. In fact the likeilhood computed is 0!
>
> You should also note that the SIR_scheme is only constructed with 10
> samples. This is very few and can also very quickly lead to on 1 sample
> being chosen in many situations. This can lead to the "Roughening X not
> PSD" exception. In practice you will usualy need more particles. Even then,
> the standard SIR is very poor at solving problem if the prediction is far
> away from the observation.
>
> Michael

-- 
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Nicola Bellotto
University of Essex
Department of Computer Science
Wivenhoe Park
Colchester CO4 3SQ
United Kingdom

Room: 1N1.2.8
Tel. +44 (0)1206 874094
URL: http://privatewww.essex.ac.uk/~nbello
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