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the equations and combined to the a a a a to the expanded state is model do find a like yeah right it is this for four in which all of the the back for some matrix is R and determinant now so we have the uh a a state vector was that in well the a region of it them from that initial point up to the present one the same for measurement and the same for and they also have a or was that matters is inside describe it or the average of her rise of off and point if you also introduce some it usual matrix is here B yeah can that you can be in need to describe i in a more correct way that the only the matrix B and then use the combinations of of of the matrix presentations to describe the model find then in chi or that and by to it as the and by that if i estimator can but it if you is high now how sound the a estimation matrix eight that actually is the gain matrix for all our measurement that wrote all the cries and fall a point and a to the present brilliant and that uh it you know why it is 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information about of the that the state space model or but be a last noise and initial and you but i the yeah it you can do this because is there is a very important G phones in the harmonic here was that doesn't across a few you have a a few of the trucks on this very large number of the points you know is that the white gaussian noise is reduced by the federal and the variance of the noise is reduced by the factor of and so if you're right the advice if you are in a frame can be a dramatically in the F I F you reduce the variance of the loss but the is the problem is this is the problem is this a that to so has and how of matrix as is that uh a just let's it is the advantages and disadvantages that than about the problem so a in the time varying of like estimator read with noise and initial conditions it have the form of bit back just it it slice there is no no no no king jeez but but at all of course "'cause" that of very strong engineering feature a a a as a as a a hand when and 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as a results more impressive there was almost no sensitivity in that F a few or to i'll by so that X that the but to as a cup few you forced to produce large stuff so so to uh what can we say a of the source so the don't known like and by it if i i estimator and noise noise and initial the is really and ice two for the optimal estimation and denotes listen and if H just for trick can state estimation and uh in a a a for the control problem the estimator i can i'll be O forms a common field or if noise and initial and uses and not a on exactly so if you do not know noise and it if you have problems and more descriptions that's try to use this you are your realise that it can provide a better result and the output a a it was this system and measurement noise components need to be if you if per out so are several problems that you need to few for power but was lost components form that uh state this model in this case that i five a few times more a my and the match to the models i'm never it in the non about phone a data or our for just our noise invited and insist case again averaging procedure is more open for a and that models have ten probably and so that a month of about and times to computation time required by the iteration procedure but no do not be necessary if you designs the field or in are little computer for every new point you just need to provide in part but the computation of the next for the next step so that this model computers because a not is a problem so that's all sent a for this become yeah now it's some quite you to that you are do not use a minimal by sec minimum Y sense so it "'cause" i i'm by you yes yes is this your there as this field or is not great converse it is a a a to she you are so it does it have i a structure finite impulse response in each case we can no feedback L i can not included this is a consist presentation because of limited time but seven as the sort and and you are descriptions that was that L equation to cook to is that the the final as a a a a a a a it's its output for it's still it not small you can use the is a a as as we see my sense and the uh the is that yeah this to so but is it a comparison is a common to for house that sake of almost the same performance in that i do or it could be a case you can all have or in the about about the model absolute absolute have as and the to few course provides or was the same result i is uh and standard standard and once we speaker in in in the is like so we are talking remote featuring problem given that the whole last all data from that from the in finding blast test at meeting and we and variance estimate uh so you you've can lean to but you are feature in you know way that not the whole plus testifying to don't top last L it's that tool there actually use a finite impulse response so it works is a finite number of the points and the past not use as a bus yeah that in contrast to the common to those it those the infinity in that that's that's what i am trying to get that we can interpret your three get as a a up to feature a minimum variance feature given a if i a blast not that in fine but well that is this presentation of leads to the and buys a finite impulse response you come like um C so was and that's of a a a a few it just a more as a minimum mean square error sense so so that it just and was the same like a the common few uh in the minimum the likelihood function so it is the same but a is this presentation leads to and buys a field i know what you're want to come now you can find and might be a a a a a what it takes time but can say in the case of that up to a few of we need to solve a take equation as but i and the averaging horizon out much larger numbers of support more questions hi the i think it can be easily extended to nonlinear ten so as like extend colour of yeah if i was yes exactly S of course you just must apply the standard extended kalman filter uh approach to use a that and apply to a nonlinear problems not problem at all and more question oh have you applied this to G P S signal no real signals you were there any well my book is devoted to G is base it up to model will F i a few or not clock models i'm do and not this position and but to the beast actually time for that actually times if few of them was designed to exactly and so and it a of it "'cause" it your exits their structure "'cause" a more change of big patient so is is so and so was a Q if you are exact in the gps S and and you get gain in a significant gain when you like this filter of four to one G S signals no yeah for for gps signal sequence but for all time incident last but time think my time was not position in and you get any gain for that significant gain over L yeah where what yes yes yeah originally we used to the common few form but as the problem is a common use the use problem if you don't know noise correctly if it meet the arrows in the noise covariance matrix but the factor of tools for or or any ah a factor if you must fix a a a it's back for a yeah and i had just rows and how experiment are we uh investigations of these applications would G P S so finally has started to find some new result and the oh i that's is it anymore more questions so unfortunately the become the third presentation is missing oh you are here are we just checked in and okay