On Jul 1, 6:02=A0am, Rune Allnor <all...@tele.ntnu.no> wrote:> Therrien suggestes that the term 'Prony method' applies to > ARMA estimation where the A() and B() coefficents are > estimated in separable steps.At the risk of embarrassing myself, since it's been literally 25 years since I worked with this stuff, I recall Prony's Method to model the impulse response of an ARMA system as the sum of damped sinusoids. There was also Pade-Prony, which did the same thing but in a z- transform formulation. Either way, the assumption of damped sinusoids would indicate that this method might be suitable for modeling of transients. It's all just a series of foggy memories. Greg

# System Identification

Started by ●June 30, 2008

Reply by ●July 1, 20082008-07-01

Reply by ●July 1, 20082008-07-01

On Jul 1, 6:02�am, Rune Allnor <all...@tele.ntnu.no> wrote:> Therrien suggestes that the term 'Prony method' applies to > ARMA estimation where the A() and B() coefficents are > estimated in separable steps.At the risk of embarrassing myself, since it's been literally 25 years since I worked with this stuff, I recall Prony's Method to model the impulse response of an ARMA system as the sum of damped sinusoids. There was also Pade-Prony, which did the same thing but in a z- transform formulation. Either way, the assumption of damped sinusoids would indicate that this method might be suitable for modeling of transients. It's all just a series of foggy memories. Greg

Reply by ●July 1, 20082008-07-01

On 1 Jul, 13:15, Greg Berchin <gberc...@sentientscience.com> wrote:> On Jul 1, 6:02�am, Rune Allnor <all...@tele.ntnu.no> wrote: > > > Therrien suggestes that the term 'Prony method' applies to > > ARMA estimation where the A() and B() coefficents are > > estimated in separable steps. > > At the risk of embarrassing myself, since it's been literally 25 years > since I worked with this stuff, I recall Prony's Method to model the > impulse response of an ARMA system as the sum of damped sinusoids.That's correct. The original works by Prony in the 1790s was on damped sinusoids, oscillations in bubbles. He formulated his solution in terms of roots of polynomial 20 years before Abel proved that roots of 5th order polynomoials could not be found by means of arithmetics, 50 years before Babbage's rudimentary computer (and 150 years before the first working computer), 15 years before Gauss came up with the least squares formulation of parameter estimation. Ah, and Prony (or Gaspard Riche, as his birthname was) was a baron in the midst of the French revolution. There are lots of reasons why Prony's work could have been forgotten over the past two centuries. His divide-and-conquer trick is the reason why his name is still remembered.> There was also Pade-Prony, which did the same thing but in a z- > transform formulation. �Either way, the assumption of damped sinusoids > would indicate that this method might be suitable for modeling of > transients.Sure. Rune

Reply by ●July 1, 20082008-07-01

On 1 Jul., 13:48, Rune Allnor <all...@tele.ntnu.no> wrote:> On 1 Jul, 13:15, Greg Berchin <gberc...@sentientscience.com> wrote: > > > On Jul 1, 6:02�am, Rune Allnor <all...@tele.ntnu.no> wrote: > > > > Therrien suggestes that the term 'Prony method' applies to > > > ARMA estimation where the A() and B() coefficents are > > > estimated in separable steps. > > > At the risk of embarrassing myself, since it's been literally 25 years > > since I worked with this stuff, I recall Prony's Method to model the > > impulse response of an ARMA system as the sum of damped sinusoids. > > That's correct. The original works by Prony in the 1790s was > on damped sinusoids, oscillations in bubbles. He formulated > his solution in terms of roots of polynomial 20 years before > Abel proved that roots of 5th order polynomoials could not be > found by means of arithmetics, 50 years before Babbage's > rudimentary computer (and 150 years before the first working > computer), 15 years before Gauss came up with the least squares > formulation of parameter estimation. Ah, and Prony (or Gaspard > Riche, as his birthname was) was a baron in the midst of the > French revolution.<nit> Prony published his algorithm 1795 [1], which is exactly the year that Gauss used least-squares to estimate the trajectory of Ceres. Gauss himself never published this, but only commented in a dismissive manner on Legendre's publication of the least-squares algorithm in 1805. Babbage proposed his Difference Engine in 1822 (Pascal's calculator was built in 1642 :-). </nit> Prony's method did not propose to use least-squares solutions for the linear problems in his three step programme to find amplitudes, damping factors, frequencies and phases of sums of sinusoids. It is a really neat algorithm that works perfectly under ideal (=noise free) conditions. Many people believe that Prony's method is in fact only the first step of his algorithm. The first step is the AR modeling, which essentially computes the linear prediction coefficients from the impulse response (or any other finite excitation). Prony's approach can easily be generalized to include least-squares solutions for the linear parts (AR coefficient estimation and phase/amplitude estimation). The major drawback is that even the extend Prony's method is super-ultra-sensitive to additive measurement noise (this comes from the polynomial factoring part of the algorithm). Sometimes, this is compensated with over-modelling (hint for Vlad) - however, use this with caution (what happens to the predictor polynomial?). As Rune said, Lawrence has a nice chapter devoted to this. If the first step in Prony's algorithm is formulated as a Kalman filter, both the state itself and the state transmission matrix (built from the AR coefficients) are unkown, leading to a non-linear Kalman representation. The extended Kalman filter in is known not to converge well in that case, the "best" approach for estimating AR signals under additive measurement noise is still open. I used a dual-linear approach in a recent project, combining a Kalman filter with a robust (against additive measurement noise) AR estimation algorithm in an iterative fashion. Messy, but works - especiall tuned for estimating the parameters from responses to impulsive excitations, where the decay of the response means that one does not have an arbitrary amount of data available for the parameter estimation. I do believe our very own Julius Kusuma's PhD thesis uses a modified version of Prony's to find parameters of sparse signals (Dirac deltas with Poisson distribute inter-arrival times). Perhaps he has more to add here. Regards, Andor [1] R. Prony, �Essai exp�rimental et analytique,� Ann. �cole Polytechnique, vol. 1, no. 2, p. 24, 1795.

Reply by ●July 1, 20082008-07-01

On 1 Jul, 14:24, Andor <andor.bari...@gmail.com> wrote:> Prony's method did not propose to use least-squares solutions for the > linear problems in his three step programme to find amplitudes, > damping factors, frequencies and phases of sums of sinusoids. It is a > really neat algorithm that works perfectly under ideal (=noise free) > conditions.There are lots of variations. The basic Prony's method formulation says that the data set contains N samples and the sum-of-sines contains N/2 (complex) sinusoidals. I don't remember the details off the top of my head, but there is also an 'extended Prony's method' and a 'modified Prony's method.' One takes into account that there are more samples than sinusoidal (e.g. dealing with over- determined systems) and the other accounts for the fact that the exact number of sinusoidals usually is not known, and thus introduces a 'model mismatch' term. And then we can start discussing exactly what terms are included in the model; frequency, amplitude, phase, damping...> Many people believe that Prony's method is in fact only > the first step of his algorithm. The first step is the AR modeling, > which essentially computes the linear prediction coefficients from the > impulse response (or any other finite excitation).Careful. There is a trick here if you deal with the standard sum-of-sines models. In order to make the maths work, one needs to use the modified covariance equations, not the usual covariance equations (details in the 1982 Proc IEEE paper by Kay and Marple). This has a number of consequences: - The covariance matrix is no longer Toeplitz - The covariance matrix is no longer unconditionally stable - The method can be used for spectrum line estimation> Prony's approach > can easily be generalized to include least-squares solutions for the > linear parts (AR coefficient estimation and phase/amplitude > estimation). The major drawback is that even the extend Prony's method > is super-ultra-sensitive to additive measurement noiseCorrect.> (this comes > from the polynomial factoring part of the algorithm).Wrong. There was a different approach suggested in 1982 by Tufts and Kumaresan. They used the same signal model as the 'usual' variations of Prony's method, but they chose a different strategy for solving for the *coefficients*, not roots, of the predictor polynomials. I tested the T&K method against one of Marple's routines in my PhD thesis. The T&K methods stil worked at SNRs 20dB lower than where the Marple method had given in. The only difference is how they compute the coefficients of the predictor polynomial from the data. Everything else is the same. Interestingly, with Vladimir's data the situation was the oposite: The results from T&K made no sense whereas the Marple algorithm apparently hit quite close to home.> Sometimes, this > is compensated with over-modelling (hint for Vlad) - however, use this > with caution (what happens to the predictor polynomial?).That's where the art lies; choosing the correct model orders. That single step makes or brakes the analysis. With Vladimir's data there are no problems, but in some applications the number of data samples impose very limiting bounds on what model orders can be chosen. Rune

Reply by ●July 1, 20082008-07-01

Vladimir Vassilevsky <antispam_bogus@hotmail.com> wrote:> > > Rune Allnor wrote: >> On 30 Jun, 21:09, Vladimir Vassilevsky <antispam_bo...@hotmail.com> >> wrote: >> >>>Rune Allnor wrote: >> >> >>>Here is the typical one: >>> >>>http://www.abvolt.com/misc/data.cppFull segment analysis can give two problems: - tail - almost only noise visible - head (about 10-20 samples) - nonlinearity or undamped higher frequency componets>> >> >> Greg already suggested Prony's method, and I agree with that.Prony at low SNR gives bad results. My best fit for Vladimir's data (full segment - 1000 points) suggests that SNR 2-pole-model/residuum is only ~17dB. You can improve estimation using only samples 20:200 - SNR 40dB> The correct answer for this example is two complex conjugate poles at > F=Fc/50 with Q = 1.7 . However I am getting wildly different results > depending on the sample rate.My favourite is Sarkar's matrix pencil method (SVD based), an implementation example: http://groups.google.pl/group/comp.soft-sys.matlab/msg/c71f1639d5253b1c My results with this function are below. Some details: - analysed segment data(20:n:200) - segment choosen by visual inspection of a plot log(abs(hilbert(signal))) - n-downsampling factor in range 1-25, for n=25 only seven samples analysed ;) - downsampling done without filtering - estimated model with 4-poles (with only 2 you can get spurious results in this case) n= 1 Q=1.647 f=0.01929 n= 2 Q=1.645 f=0.01929 n= 3 Q=1.645 f=0.01929 n= 4 Q=1.646 f=0.01930 n= 5 Q=1.644 f=0.01929 n= 6 Q=1.644 f=0.01929 n= 7 Q=1.644 f=0.01931 n= 8 Q=1.647 f=0.01930 n= 9 Q=1.649 f=0.01934 n=10 Q=1.638 f=0.01926 n=11 Q=1.642 f=0.01928 n=12 Q=1.650 f=0.01930 n=13 Q=1.660 f=0.01930 n=14 Q=1.640 f=0.01928 n=15 Q=1.637 f=0.01925 n=16 Q=1.667 f=0.01942 n=17 Q=1.643 f=0.01929 n=18 Q=1.673 f=0.01938 n=19 Q=1.673 f=0.01941 n=20 Q=1.632 f=0.01928 n=21 Q=1.701 f=0.01934 n=22 Q=1.763 f=0.01927 n=23 Q=1.477 f=0.01916 n=24 Q=1.671 f=0.01929 n=25 Q=1.766 f=0.02000 Mirek Kwasniak

Reply by ●July 1, 20082008-07-01

Rune Allnor <allnor@tele.ntnu.no> wrote in news:8f2a3224-efa0-461e-904b- ded21d38864d@a1g2000hsb.googlegroups.com:> As far as I can tell, 'Prony's method' applies to dividing > the multiple-parameter estimation problem into separate > steps where each parameter is estimated independently > from the others (from a numerical sense). >Isn't this also known as "curve peeling"? -- Scott Reverse name to reply

Reply by ●July 1, 20082008-07-01

On 1 Jul, 12:02, Rune Allnor <all...@tele.ntnu.no> wrote:> On 1 Jul, 05:14, Vladimir Vassilevsky <antispam_bo...@hotmail.com> > wrote:> > > I have a version lying around which was based on stationary > > > data (whereas your data is a transient.) > > > What is fundamentally different between the cases of impulse response > > and the stationary data? > > The *theoretical* discussions in Therrien doesn't make the > distinction. Practical implementations are very different > in that they are based on different data models. The one > I used yesterday was based on a sum-of-sinusoidals model > on the form > > � � � � P > x[n] = sum a_p exp (j w_p n + theta_p) > � � � �p=1 > > The model does not include noise nor exponential damping > terms. When I implemented that processor I had a book > available where a number of variations was available, > including one which included the exponential damping terms.This thread got me thinking. First of all, the Marple book, published a mere 20 years ago has been out of reach for over 15 years already. The Tufts&Kumaresan methods which beat Marple hands down didn't work. So I went back to look over the T&K methods, and sure enough, the methods I used for my PhD thesis were configured for the forward-backward prediction problem. Once I adjusted them for forward-only prediction, Prony's method based on the T&K implementation worked like a charm. Vladimir, if you are intersted in discussing this in more detail in private, drop a hint here. If you post from a valid email address I'll send you a valid email address of mine via my google posting account; the email address in my posts has been obsolete for years. Rune

Reply by ●July 1, 20082008-07-01

pisz_na.mirek@dionizos.zind.ikem.pwr.wroc.pl wrote:> Vladimir Vassilevsky <antispam_bogus@hotmail.com> wrote: > Here is the typical one: > >http://www.abvolt.com/misc/data.cpp > > > Full segment analysis can give two problems: > - tail - almost only noise visible > - head (about 10-20 samples) - nonlinearity or undamped higher frequency > componets > > Prony at low SNR gives bad results. > > My best fit for Vladimir's data (full segment - 1000 points) suggests that > SNR 2-pole-model/residuum is only ~17dB. > > You can improve estimation using only samples 20:200 - SNR 40dBI checked; this is not the SNR issue. The approach was incorrect: I was trying to build Wiener filter by AR method without having the meaningful data sufficiently long. So it worked very well for the longer impulse responses (Q ~ 10) and goofed for the shorter ones.> My favourite is Sarkar's matrix pencil method (SVD based), > an implementation example: > http://groups.google.pl/group/comp.soft-sys.matlab/msg/c71f1639d5253b1cWhat book would you suggest on this method?> Some details: > - analysed segment data(20:n:200) - segment choosen by visual inspection > of a plot log(abs(hilbert(signal)))You have my respect for log|Hilbert| envelope. It is nice to see the professional approach.> - n-downsampling factor in range 1-25, for n=25 only seven samples analysed ;) > - downsampling done without filtering > - estimated model with 4-poles (with only 2 you can get spurious results > in this case) > > n= 1 Q=1.647 f=0.01929[...] Neat. Right on target.> Mirek KwasniakThank you, Dr. Kwasniak Vladimir Vassilevsky DSP and Mixed Signal Design Consultant http://www.abvolt.com

Reply by ●July 1, 20082008-07-01

Rune Allnor wrote:> This thread got me thinking. First of all, the Marple book, > published a mere 20 years ago has been out of reach for > over 15 years already. The Tufts&Kumaresan methods which > beat Marple hands down didn't work.Just returned from the local univ. library. Marple is not there.> So I went back to look over the T&K methods, and sure > enough, the methods I used for my PhD thesis were configured > for the forward-backward prediction problem. Once I adjusted > them for forward-only prediction, Prony's method based on > the T&K implementation worked like a charm.The problem was because the impulse response was too short for using the AR method as a brute force.> Vladimir, if you are intersted in discussing this in more > detail in private, drop a hint here.I would certainly be interested.> If you post from a valid > email address I'll send you a valid email address of mine > via my google posting account; the email address in my > posts has been obsolete for years.My valid email and phone contact are at the web site in the signature; please send from some account other then gmail: the gmail is likely to be filtered out by spamkillers.> > RuneVladimir Vassilevsky DSP and Mixed Signal Design Consultant http://www.abvolt.com