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Various Signal Processing Techniques used on non-Stationary Acoustic Doppler Current Data. Volume I: XI
Responsible organisation
1999 (English)Report (Other academic)
Abstract [en]

Chapter 1 - Background, deals with the process of analyzing the backscattering signal transmitted from an ultrasonic transducer, [5][28]. The narrowband sinusoidal burst signal is Doppler-shifted due to the current, and this information is converted into current, [14]. The traditional mathematical model for this Doppler process is based on the assumption that the backscattering time signal is Gaussian, due to the Rayleigh backscattering amplitude assumption with random phase, [23][27]. This is based on the assumption that the backscattering is due to many randomly distributed bubbles with about equal size. It is reasonable to question whether this assumption holds for real life signals, [ 1][7][8]. Therefore, this work has concentrated on looking at real life data, and has investigated whether the Gaussian assumption holds for the background noise and the Doppler signal received. It has been found that this is not generally the case. Chapter 2 - Spectral Analysis of Data, provides analysis of the spectral content in the data using tools with different properties. The reason is the difficulty in distinguishing real spectral peaks in the data from peaks coming from variance in the estimate, [2][3]. Therefore, 3D-plots have been generated of current data from four locations around the world with very different environments. Also, a non-linear filtering method named Multiple Peak Count Analysis, MPCA, has been developed. This analysis is most important in understanding if there is more than one Doppler signal component (current) active in the measurement cell analyzed. Using these two methods, which use different foundations for the analysis, it is possible to determine if, and often, how many, Doppler signals are active in one cell. This compares to how many spectral peaks the data contains for each observation interval. Chapter 3 - Statistical Measures provides an analysis of the data using classical statistical tools like histo.gram, normal probability plots, Chi-square tests and variance analysis like ANOVA, ANalysis Of Variance, [2 1][26]. These tools helps in understanding if it is possible to use a Gaussian approach for the signal model, or if some other distribution could be better suited. Data from all four locations are used in the analysis and key results are presented. In this chapter, analysis of the background noise is also analyzed and presented using the above statistical measures. Chapter 4 - Higher Order Moments, provides a description of higher order moments using skewness y, and kurtosis y2. These are important tools of the statistical behavior of the data analysis. The .investigation of the higher order moments for the time series of the three ADCMs, does not contradict the proposed signal model. Furthermore, the real world signals converge very much to what can be expected if this new model is adequate for this kind of signal. The conclusion is, then, that the model holds for this test. The data is found not to obey the Gaussian signal model in general. This is particularly true when the water is troubled. A comparison with real data from four different locations presented above has been performed and all data shows the same trends, the data cannot be modeled using Gaussian statistical properties. The 3D plots presented earlier show that there often are several current vectors active in a cell at the same time, and this has a strong effect on the statistics for the time signal, which is quantified in this chapter. Chapter 5 - Comparison of Estimators, provides an extensive comparison between the covariance method and the Symmiktos MethodTM. Simulated and real data from all four locations have been used in the comparison. The comparison is presented in several formats to make conclusions easier. It is clear that the Symmiktos Method*M generates quite different results from those of the covariance method. On simulated data, the Symmiktos MethodTM is much closer to the simulated truth. However, in real life we don’t know the answer, so it is impossible to be sure which estimator is more accurate. Based on the results from the simulated signals and also noticing that the variance is lower when using the Symmiktos Method M, plus adding the results from the signal model together, it is fairly safe to argue that the Symmiktos Method*M is a more robust and accurate method for Doppler frequency estimation on this type of data. Chapter 6 - Estimator Programs, gives a brief background on the imple-mentation of the main Matlab programs used in the calculations, and the most important programs for understanding of the work, are listed. The programs listed are not only the statistical programs but also the programs used for testing a new signal model and comparing the covariance method with the Symmiktos Method. Chapter 7 - Description of Used Data Sets, gives a brief background on the data sets used in this research. Key data from the four locations is given as well as all the background parameters to when and how the data was collected, as well as the main observations made at the time of data collection. ) Chapter 8 - Summary and Conclusions, provides a summary of the key results from the four different locations. Each method is commented individually and the main effects are discussed. Chapter 9 : References, lists all the references used. Chapter 10 - Listing of Measurement Plots, lists all the plots. The plots consist of about 2000 pages divided over 11 volumes.

Place, publisher, year, edition, pages
1999.
Series
Blekinge Tekniska Högskola Forskningsrapport, ISSN 1103-1581 ; 16
Keyword [en]
Doppler, Current, Estimation, Shte symmiktos method, ADCP, ADCM, Gaussian Backscattering Model, Acoustic Doppler, Doppler Profiler, MPCA
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-00127Local ID: oai:bth.se:forskinfo469D2F1DD3325AACC12568A3002CAC1EOAI: oai:DiVA.org:bth-00127DiVA: diva2:838101
Note
This research report consists of eleven volumes. The content of each volume is described below, Volume I. l.Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch l -Page 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 1 -.Page 1 1.2 Short description of used data sets. . . . . . . . . . . . . . . . . Ch 1 - Page 3 1.3 List of notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch l -Page 5 1.4 Systembackground. ... . . . . . .. . . . . . . . . . . . . . . . . . Ch 1 -Page 7 2. Spectral Analysis of Data . . . . . . . . . . . . . . . . . . . ......... Ch 2 -Page 1 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 2 - Page 1 2.2 Used Data Set .... . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 2 -Page 3 2.3 Spectral Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 2 - Page 5 2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 2 - Page 5 2.3.2 Non-linear Pre-filtering of the Spectral Data . Ch 2 - Page 9 2.4 Multiple Peak Count Analysis, MPCA . . . . . . . . . . . . Ch 2 - Page 11 2.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . Ch 2 - Page 11 2.4.2 Peak Detection . . . . . . . . . . . . . ... . . . . . . Ch 2 -Page 11 2.4.3 Difference equation . . . . . . . . . . . . . . . . . . . Ch 2 - Page 13 2.4.4 The Floor .........,............................................ Ch 2 -Page15 2.4.5 Double Peaks . . . . . . . . . . . . . . . . . . . . . . . . . Ch 2 - Page 15 2.4.6 Quantification . . . . . . . . . . . . . . . . . . . . . . . Ch 2 - Page 17 2.4.7 Results from the MPCA Calculation . . . . . . . Ch 2 - Page 19 2.5 3 D MPCA Plots of.Data . . . . . . . . . . . . . . . . . . . . . . . Ch 2 - Page 25 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 2-Page 45 3. Statistical Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 3 - Page 1 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 3 -Page 1 3.2 Used Data Sets . . . . . ...... .. . . . . . . . . . . . . . . . . . Ch3-Page3 3.3 ANOVA Tests . . . . . . . . . . . . . . ................. Ch3-Page5 3.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch3-Page5 3.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 3 - Page 8 3.4 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 3 - Page 17 3.4.1 Background . . . . . . . .. . . . . . . . . . . . . . . . . . . . Ch 3 - Page 17 t 3.4.2 Several distributions or mixed signals . . . . . Ch 3 - Page 23 3.4.3 Results: Histograms . . . . . . . . . . . . . . . . . . . . Ch 3 - Page 25 3.5 Normal Probability Tests ........ ............. Ch 3 - Page 33 3.5.1 Background .......................... Ch 3 -Page 33 3.5.2 Results: Normal Probability Plots ... ..... Ch 3 - Page 35 3.6 Chi-2 Tests ................................. Ch 3 -Page 43 3.6.1 Background ........................... Ch 3 -Page 43 3.6.2 Results: Chi-2 Tests, 2-Dimensional ..... Ch 3 - Page 45 3.7 Chi-2 Tests, 3-Dimensional .................... Ch 3 - Page 53 3.7.1 Results: Chi-2 Tests, 3-Dimensional ....... Ch 3 - Page 53 3.8. Background Noise .............................. Ch 3 -Page 61 3.9 Summary ................................... Ch 3 -Page 67 3.10 Table of Contents ........................... Ch 3 -Page 71 4. Higher Order Moments ............................... Ch 4 - Page 1 4.1 Background ................................... Ch 4 - Page 1 4.2 Used Data Sets ................................ Ch 4 -Page 3 4.3 Theoretical Background ...... ................. Ch 4 - Page 5 4.4 Higher Order Moments for the Proposed Model ..... Ch 4 - Page 9 4.5 Comparison with Real World Signals ............ Ch 4 - Page 17 4.6 Results from Higher Order Moments .............. Ch 4 - Page 2 1 4.7 Summary ................................... Ch 4 - Page 29 5. Comparison of Estimators ............................. Ch 5 - Page 1 5.l Introduction.. ................................ Ch 5 - Page 1 5.2 Used Data Sets ............................... Ch 5 - Page 3 5.3 Comparisons Using Simulated Signals ............. Ch 5 - Page 5 5.4 Comparisons Using Real Life Data ............... Ch 5 - Page 9 5.5 Sumrnary ................................... Ch 5 - Page 19 6. Description of Matlab Programs ........................ Ch 6 - Page 1 6.1 Introduction .................................. . . Ch 6 - Page 1 HIST_D0P.m:. .......... :.. ....................... Ch 6 -Page 5 PL6_ADCM.m: .................................. Ch 6 - Page 6 SP3_ADCM.m: ................................... Ch 6 -Page 9 SPA_ADCM.m: ............................ Ch 6 - Page 11 SP4_ADCM,m: .................................. Ch 6 - Page 15 SP_ADCM.m: .................................. Ch 6 - Page 17 N0RMERA.m: ................................. Ch 6 - Page 18 PL8_ADCM.m: ................................. Ch 6 - Page 19 SP5_ADCM.m: ........... .. .......... .. Ch 6 - Page 22 SP_TEST.m: . . . . . . . . . . . . . . . . . . . . . . . . . :. . . . . . . . . Ch 6 - Page 24 P.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6-Page 25 RD_ADCM.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 29 X300ADCM.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 -Page 32 PL4_ADCM.m: . . . . . . . . . . . . . . . .. . . . . . . . .. . . . . . . . Ch 6 - Page 33 S.m: ...:... . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 35 SP7_ADCM.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch6-Page36 CL.m:........:................................ ....................................Ch 6 - Page 38 PLS_ADCM.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 39 SP2_ADCM.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 40 SP8_ADCM.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 41 SPA_ADCM.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6-Page 44 SPA_FFT.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6-Page 45 SOL-SPA.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 46 ar_fft.m: . . . . . . . . . . . . .. ... . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 47 peak_det.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 48 pll0.m:......................................................................... ..... Ch 6 - Page 49 plll..m:........................................ ...................................Ch 6 - Page 52 p19.m:......................................... ..................................Ch 6 - Page 56 p192.m:........................................ ................................Ch 6 - Page 59 Spl0.m:.. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 60 sp102.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 63 sp1l.m: ....................................... ..............................Ch 6 - Page 64 spl12.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 67 sp9.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 68 sp9i.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 71 chs1ope.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 72 p112.m:........................................ ......................................Ch 6 - Page 76 sinfft12.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 -Page 80 sp12.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 81 sp122.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 84 chView.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Pag e85 rView.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 87 getNameStr.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 88 pf.m:........................................................................... ..... Ch 6 - Page 90 sf.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 96 sfa.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 97 skipBs.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 98 surfi.m: ................. . . . . . . . . . . . . . . . . . . . . . . ................Ch 6 - Page 99 meshi.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 100 anova.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 101 arg .m :........................................ .....................................Ch 6 - Page 102 cSurfi.m: . . . . . . . . . . . . . . . . . . . . . . . .............. Ch 6 - Page 103 pol.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 105 shpeak.m: . . . . . . . . . . . . . . . . . . . . . . . .‘ . . . . . . . . . . . .Cb.6 -Page 106, shpol.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 107 cMoment.m: . . . . . . . . . . . . . . . . . . . . : . . . . . . . . . . . . . . Ch 6 - Page 108 gamma1.m: . . . ... . . . . . . . . . . . . . . . . . . . . .. . . . . . . Ch 6 - Page 109 gamma2.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 110 cut.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... Ch 6 - Page 111 disp1ot.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 112 gScan.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... Ch 6 - Page 113 pingP1ot.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 -Page 114 maxp.m: ..................................................................... Ch 6 - Page 115 call_mHist.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 -Page 116 mHist.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 117 an.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 119 ma.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 121 DoA1ma.m: . . . . . . . . . . . . . . . . . . . . . :. . . . . . . . . . . . . Ch 6 - Page 122 chi2_2d.m:.................................... .............................Ch 6 - Page 126 Chi2test.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 -Page 129 getName.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 130 read-asc-adcm.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 132 read-adcm.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page133 sp1itping.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 -Page 134 chi2onArray.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 135 chi2_3d_bar.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 136 mp.m: ........................................ ......................................Ch 6 - Page 139 hi.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 140 phi.m: . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 141 nd.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 144 ti.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 145 pti.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 146 adcm.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 148 adcm_loop.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 153 adcm_cov.m: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 6 - Page 157 adcm_sym.m:..................................................................... . Ch 6 - Page 159 7. Description of Used Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . Ch 7 - Page 1 7.1 Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 7 -Page 1 7.2 The Trubaduren Light House . . . . . . . . . . . . . . . . . . . . Ch 7 - Page 5 7.3 FIaden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 7 - Page 7 7.4 AImagrundet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 7 - Page 9 7.5 Ma-Wan ,Hongkong ........................... C h 7 - Page 11 7.6 DataFormat ... .............................. ........Ch 7 - Page 13 8. Summary and Conclusions ............................. Ch 8 - Page 1 8.1 Main reason for the work ......................... Ch 8 - Page 1 8.2 Key results ................................... Ch 8 - Page 3 8.3 Future work .: ................................. Ch 8 - Page 5 9. References .. . . . . . . . . . . . . . . . . . . . ... Ch 9 - Page l-2 10. Listing of Measurement Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch 10 Time domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Pages 3D Spectrogram plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Pages Volume II. Normal probability plots, part 1 . . . . . . . . . . . . . . . . . . . . . . . . 254 Pages Volume III. Normal probability plots. part 2...................................................254 Pages Volume IV. Histogram plots,.part l . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180Pages Volume V. Histogram plots, part 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Pages Volume VI. ANOVA plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Pages Gamma plots . . . . . . . . . . . . . . . . . . . . ‘ . . . . . . . . . . . . . . . . . . . . 32 Pages 3D Multiple Peak Count Analysis, MPCA . . . . . . . . . . . . . . . 134 Pages Volume VII. Multiple Peak Count Analysis, MPCA, part 1 ..............................192 Pages Volume VIII. Multiple Peak Count Analysis, MPCA, part 2 ..............................192 Pages Volume IX Multiple Peak Count Analysis, MPCA, part 3 ..............................192 Pages Volume X. Chi-2, 2D Plots........................................................................... ..........158 Pages Chi-w, 3D Plots........................................................................... ...........58 Pages Volume XI. Comparison Covariance-Symmiktos..................................................133 PagesAvailable from: 2012-09-18 Created: 2000-03-15 Last updated: 2015-06-30Bibliographically approved

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