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alimohebbi
@alimohebbi
code are in the book
but get this erros
File "/usr/local/lib/python2.7/dist-packages/scipy/_lib/_util.py", line 231, in _asarray_validated
raise ValueError('object arrays are not supported')
ValueError: object arrays are not supported
what should i do?
Matthias
@maluethi
@rlabbe: I am reading through your book online and so far it has been amazing, thanks for publishing all this!
Although I did not read all chapters yet, it came to my understanding that outliers (measurements containing no information) are pretty bad for a 'standard' Kalman filter. By just searching trough the pdf version I didn't find any mention of outliers. So I was wondering if you could point me to any resources that introduce outlier handling within kalman filtering? Of course I googled but I cannot really judge the quality for a beginner of all the different results I get back...
Roger Labbe
@rlabbe
@maluethi , sorry, I haven't logged in here for a long time.
I intend to add a section on just this topic in a few day. In the meantime, I suggest looking up 'mahalanobis distance', which is a measure of how far a measurement is from the KF's prior. You can use this to 'gate' your data - discard data that is "too far away". Theory says throw away anything > 3 std, but in practice you may find 4,5, even 6 std to be a better gating distance
If you throw the data away, you just don't call update for that time period. You will thus call predict twice in a row, and your estimate will gain uncertainty because you did 2 predictions in a row
that's the general idea. The search term "kalman filter gating" is also a fruitful search.
Roger Labbe
@rlabbe
@maluethi the book is updated with a section on outliers. see chapter 8.
Matthias
@maluethi
@rlabbe Thanks a lot for letting me know. I will certainly look into the new chapter!
noemecd
@noemecd
I wish to congratulate you for your excellent book on Kalman and Bayesian filters.
It is clear, didactic and well-documented.
I had to build my very first Kalman filter in a quite complex configuration (7 state variables, strong non-linearities and very low signal to noise ratio).
Your book has brought me tremendous help in doing that - although, as a Scilab user, I have found the recourse to Python more troublesome than helpful -.
It is rare to find such a thorough, simple, user-oriented while scientifically sound presentation of the Kalman filter.
I think you are a born pedagogue.
Lots of thanks.
Miguel Oyarzun
@Miguel-O-Matic
@rlabbe I just started working through the Jupyter notebooks. Your intuitive approach to the subject matter is very refreshing. I do have a question/observation about the material in Chapter 3... I find the discussion of the product vs sum of "Gaussians" a bit confusing. It seems that you are discussing the sum of Gaussian random "variables" and the product of Gaussian probability "distributions". The sum of two independent Gaussian random variables is also Gaussian-distributed. The product of two Gaussian random variables is not, in general, Gaussian-distributed.
Miguel Oyarzun
@Miguel-O-Matic

@rlabbe I just started working through the Jupyter notebooks. Your intuitive approach to the subject matter is very refreshing. I do have a question/observation about the material in Chapter 3... I find the discussion of the product vs sum of "Gaussians" a bit confusing. It seems that you are discussing the sum of Gaussian random "variables" and the product of Gaussian probability "distributions". The sum of two independent Gaussian random variables is also Gaussian-distributed. The product of two Gaussian random variables is not, in general, Gaussian-distributed.

Having now made it through Chapter 4... I think the source of the confusion is that, in both cases, we are really talking about operations on Gaussian "distributions" rather than random "variables" . The mathematical operation involved in the "prediction" step is really a convolution, rather than a sum, of Gaussian "distributions", which can be shown to be a Gaussian "distribution" with mean and variance as described in Chapter 3. At least, that's what I think after reading Chapter 4... Looking forward to further enlightenment in the upcoming chapters... :-)

han-so1omon
@han-so1omon
Thank you for writing this book. I hope it is valuable to students, as I wish that I had it when I was a student.
Douglas Daly
@douglas-daly_gitlab
Wow - what an amazing book! You offer a very clear and conceptual approach to these topics without glossing over the foundations. And of course, presenting as Jupyter notebooks is a huge help and removes the mysteries of tuning parameters and such.
gabrielegranello
@gabrielegranello
Thank you very much for providing the library and the book, both have been of immense value for me to understand how Kalman filters work.
dangle1
@dangle1
Is there anyone here interested in forming a study group for this book? I'm working my way through it and understand it for the most part, but think studying together would make learning better.
Rachel Cohen Yeshurun
@rachelyeshurun
Wow, just amazing notebooks, hope one day to work through this. In the meantime, this notebook series is my inspiration for my own work.. Question for you @rlabbe or anyone who knows: I haven't been able to find out how to show all output on startup. I see all your notebooks when rendered on Binder too, show up with all the plots rendered, and no cell output number. It looks so clean, how do you do that??? My cells just show the code. Markdown is rendered, but code is not run, even though I ran all and saved before commit.
MichaelHay42
@MichaelHay42
Hi @Rlabbe, just starting to read your book (in chapter 1) it is FANTASTIC, i am learning so much, it is a delight! About filters yes, but also I didn't know I could run Jupyter notebooks on Azure and Binder. Very cool! THANK YOU so much!!
laurelstrelzoff
@laurelstrelzoff
Hi @rlabbe , I'm trying to implement a particle filter (SIR) like the one in chapter 12, but I'm having difficulty with it. I'm using it for bearings only tracking of multiple targets to differentiate between true and false targets, and I haven't been able to adapt that code to work without landmarks. Do you have any advice?
fanggenzaiXHBS
@fanggenzaiXHBS
Hi, @rlabbe , you call the difference between the measurement and prediction is called the residual, I don't quite agree with you. I mean the difference between the measurement and prediction is called the innovation.the difference between the measurement and posterior is called residual.
1 reply
sjphilli
@sjphilli
@rlabbe Nit: Chapter 3.2 is titled "Mean, Variance, and Standard Deviation", however none of these are mentioned in 3.2. Suggest 3.2 be renamed to "Random Variables" and 3.2.1 be removed.
fsi145686
@fsi145686
@rlabbe Dear author, I have a question. If the measurement function is unknown in the state space, it is a parameter, which method can be used to estimate the measurement function?
Prashant Dandriyal
@pra-dan
Hi guys.
Prashant Dandriyal
@pra-dan

In chapter 6: Multivariate Kalman Filters, in the part

what if 𝑥=𝑥˙Δ𝑡 ? (set F00 to 0, the rest at defaults)

leads to the following plot of 𝐏=𝐅𝐏𝐅𝖳𝐏=𝐅𝐏𝐅^𝖳:

https://imgur.com/a/UbLEJuw

But I can't get the idea behind it, I mean how is the variance for x (position) reduced to minimum ? On using 𝑥0 = 0, the resulting matrix for P is:

Font metrics not found for font: .:  
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Prashant Dandriyal
@pra-dan
Can anybody help ?
Prashant Dandriyal
@pra-dan

Hi @rlabbe I came across the part "Stable Computation of the Posterior Covariance" and couldn't understand how the Joseph-equation-derived-covariance mitigates a non-optimal K (Kalman gain) ? I see that to be possible only when a different method is used to compute K.

Can you please help ?

Shiladitya Biswas
@notu97
@rlabbe in the g-h filter chapter, when we introduce the change in gain_rate as well, in the update step why are we using the residue only, i.e.
why the the gain_rate equation given by "gain_rate = gain_rate + gain_scale (residual/time_step)", and not "gain_rate = gain_rate + gain_scale ((residual-gain_rate)/time_step)", here 1 is our predicted gain rate. Shouldn't the residual give us the measured gain_rate (from sensor) and we already have our previous gain_rate. Using both of these , we update our next gain_rate.
please clarify