Effect of Samples on PSE Using Welch Method

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1094International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882

Volume 3, Issue 8, November 2014

Effect of Samples on PSE Using Welch Method

Ravi Kumar Anand1, Rajesh Mehra

2

Electronics and Communication Department, NITTTR, Chandigarh, India.

AbstractVariation in data sequence length may changethe performance of power spectrum estimationtechnique. In this paper PSE for Bartlett windowtechnique for different samples has been perform

using Welch method, which is a non-parametricmethod or some times called averaging modified periodogram technique. This non-parametric PSEapproach gives good resolution result if datalength or number of samples has been chosen in

a precise way. This paper gives the matlab resultfor Bartlett window technique using Welchmethod for different samples under considerationand shows that increased finite number ofsamples gives better resoluted result compared toless number of samples.

Keywords- Bartlett window, PSD, PSE, Quality factor, Welch method.

I. INTRODUCTION

In this paper we wish to show the effect ofsample variation on PSD with the help of Welchmethod for Bartlett window. The basic problemthat occurs is the estimation of power spectral

density of a signal from the observation of thesignal over finite time interval. The finite recordlength of the data sequence is a major limitation

on the quality of the power spectrum estimation[1].The non-parametric Welch in which the power of any point is guesstimation at differentfrequencies [2].When dealing with the signalsthat are statistically stationary, the longer datarecord, the better the estimate that can beextracted from the data. On the other hand if the

signal statistics are non-stationary we can notselect an arbitrarily long data record to estimatethe spectrum. Here in this paper we plot the PSE

graph for the sum of two sinusoidal waveformwith noise content and its variation over distinctsamples. Bartlett window has admirableresolution characteristics for sinusoids ofcomparable strength Bartlett method provide amethod to reduce the variance of periodogram in

exchange for a reduction of resolution incompared to standard periodogram [3].Welchmethod is a modified version of periodogram orBartlett in which portion of the series

contributing to each periodogram are used tooverlap. Here all estimations come under 50%overlapping of each segment of window which isthe conventional way to use Welch method for better resolution.

II. POWER SPECTRUM ESTIMATION

Power spectrum estimation concerned with thespectral characteristics of signal characterized as

random process many of the phenomena thatoccur in nature are best characterized statisticallyin terms of averages. For example, meterological phenomena such as fluctuations in airtemperature and pressure are best characterizedstatistical random processes. Power spectrumestimation methods have a relatively long

history. For a historical perspective , the reader isreferred to the paper by Robinson(1982) and the book by Marple (1987).the classical powerspectrum estimation methods based on the periodogram, originally introduced by Schuster

(1898) and by Yule(1927), who originated themodern model-based or parametric methods.

These methods were subsequently developed andapplied by Walker(1931), Bartlett(1948),Parzen(1957), Blackman and Tukey(1958),

Burg(1967) and others. One of the problems thatwe encounter with classical power estimationmethods based on a finite-length data record isthe distortion of the spectrum that we areattempting to estimate. This problem occuers in both the computation of the spectrum for adeterministic signal and the estimation of the power spectrum of a random signal. Since it iseasier to observe the effect of the finite length ofthe data record on a deterministic signal.

a. Welch method

The following two modifications were made byWelch in 1967 in the averaging periodogram.

(1) The subsequence of x(n) are applied tooverlap.

(2)

A data window w(n) is applied to eachsubsequence in computing the periodograms.



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If we are talking about Welch method, theoverlapping subsequence are represented by

Xi(n)=x(n+iD), n=0,1,-----M-1

I=0,1,-----L-1 (1)

iD is nothing but starting point of the sequence.

Observe that if D=M, segments do not overlap

and the L of data segment is identical to numberK in Bartlett method.

If D=m/2 there is 50% overlapping betweensuccessive data segments and L=2k segments areobtained. Alternatively we can form K datasegments each of length 2M.

Again in second modification made by Welchmethod to Bartlett method is to window tha datasegments prior to computing the periodogram ,the result is a “modified” periodogram.

(2)

Where U is a normalized factor for the power inthe window function and is selected as-

(3)

The Welch power spectrum estimate is theaverage of these modified periodogram, that is-

(4)

The mean value of the Welch estimate is-

(5)

in case of 50% overlapping between successive datasegments (L=2K), the variance of the Welch power

spectrum estimate with the triangular window alsoderived in the paper by Welch is-

var[pxxw

(f)] = 9/82 Γ2XX(f) (6)

the quality factor of Welch method may be written as-

L = N/M for no overlapping

8L/9 = 16N/9M for 50% overlapping (7)

The spectral width of triangular window is at 3Db point is given as-

Δf = 1.28/M (8)

The quality factor expressed in terms og N and Δf

(9)

Variance of Welch method is given as----

(10)Properties of Welch method-

ˆ j 1 k 1 L1 jn

2

x(n W e ) iD)e KLU i0 n0

ˆ 1 k 1

ˆ (i) 1 L1 2

P W (e ) M (e ) , U w(n)

Bias

L i0

L n0

ˆ 1 j j 2 E { P B (e )} P x (e ) W (e )

2 LU

TABLE I. Non-parametric methods of powerspectrum estimation

III. RESULTS

In this paper we are trying to show the effect ofvariation of number of samples on resolution of

Welch method.Here we are adding two sinusoidalwaveforms plus noise under sampling frequency fs =1000 and 50% overlapping.

Again here we are taking Bartlett window as areference and start with N = 128 and fs = 1000, we

get result as-



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Again start with Bartlett window as a reference, thistime we take N = 512, fs = 1000, we get result as-

Again here we start with Bartlett window, but thistime we take N = 1024 and fs = 1000, we get theresult as-

IV. CONCLUSION

On the basis of the result that we have shown N=128,N=512 and N=1024, we can make theconclusion based on the observation that N=1024,gives the better resolution and variance compared to N=128 and N=512. This shows that Welch method is

suitable if we increase Number of samples or can saythat this method gives better Result if we increasesamples. Comparison have done with Bartlett windowwhich alsogives the result that it have clear peak aswe increase the number of samples as shown ingraph.

Thus we can conclude the whole result that Welchmethod gives psd of a signal with reducing the effect

of noise &work in a better way if we increase thenumber of samples.

REFERENCES

[1] John G. Proakis dimitris G. manolakis,“Digital signal Processing principles,algorithms, and applications”, Prentice-HallIndia, Third edition 2005.

[2] Emmanual C. Ifeachor, Barrie W.Jervis,”Digital signal Processing a practicalapproach”, Pearson Education, SecondEdition

[3]

Hansa rani gupta,”Power spectrum estimationusing Welch method for various windowtechniques” Internation-al journal ofscientific research engineering &technology,volume-2, September 2013.

[4] S.K. Mitra, Digital signal processing, Tata

Mc Graw Hill, Third Edition, 2006[5] http://www.slideshare.net/contactsarbjeet/bart

lettsmetho hod-pp-ts(10-11)

[6] B.Porat,a course in digital signal processing,John Wiley, 1997.

[7]

D.J. Thompson, “spectrum estimation andharmonic Analysis”, proc. IEEE, vol.70.

Effect of Samples on PSE Using Welch Method

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