IEEE Seventh SP Workshop on Statistical Signal and Array Processing最新文献

{"title":"Invariant Tests For Spatial Stationarity Using Covariance Structure","authors":"S. Bose, A. Steinhardt","doi":"10.1109/SSAP.1994.572451","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572451","url":null,"abstract":"We propose new tests for validating the ULA model based on the received array data. These tests use the structure of the covariance matrix of the data to decide whether the received data is spatially stationary. Invariance principles are used to ensure that the test has constant significance (equivalent to a CFAR test) when the model is indeed true by eliminating the dependence on the covariance. In addition, a modification of the test is proposed for verifying stationarity within a specified subspace, and heuristics are presented for distinguishing propagation effects from deviation from the ULA model.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127114568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-resolution Two-dimensional Direction Finding For Uniform Circular Array","authors":"A.Y.J. Chan, J. Litva","doi":"10.1109/SSAP.1994.572466","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572466","url":null,"abstract":"This paper concerns the application of multiple signal classification (MUSIC) and maximum likelihood (ML) techniques to the joint azimuthal and elevational directions-of-arrival (AEDOA) estimation with a uniform circular array. The deterministic and the random source signal models are used. Computer simulations and theoretical predictions are provided to compare the MUSIC and ML performance. It is shown that the unconditional ML method outperforms the deterministic ML method, which in turn outperforms the MUSIC method.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"18 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131351878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral Correlation Applied to Gear Monitoring in Electrical Power Plants","authors":"B. Georgel, P. Prieur, G. Calot","doi":"10.1109/SSAP.1994.572544","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572544","url":null,"abstract":"In power plants numerous equipments including gears have to be monitored to prevent damage and non-planned shut-downs. Whereas only visual or aural monitoring by skilled personnel is performed presently, studies are carried out to improve this monitoring by processing vibratory signals provided by sensors. These signals exhibit both amplitude and phase modulations and so can be processed by the spectral correlation approach. We have compared the Wigner-Ville distribution and the spectral correlation and showed that the latter is well adapted to modulated signals analysis. It allows for discriminating between running-in phases, stable phases and aging or damage phases of the component to be monitored. The ability to show in advance signs of degradation through modulation rates changing and also modulation structure variations is demonstrated on real life signals. 1. GEAR MONITORMG M POWER PLANTS Numerous machines in power plants (e.g. rotating machines) contain gears which have to be monitored in order to prevent damage and shut-downs. Today's state of the art in monitoring is based on periodic maintenance. This is not optimal neither technically nor economically. Conditional maintenance will probably replace it, providing that we are able to measure reliable and relevant descriptors of the vibrational behaviour of the component and to derive from them a consistent indicator of degradation. This will finally be used to stop the machine before damage has occurred. The principle of the gear monitoring is to get external signals from accelerometers stucked on the gear box and to analyse them so as to determine whether the gear teeth are damaged or not. correlation which appear to be well suited to signals from rotating machinery. Different descriptors computed from the spectral correlation will be introduced whereas section 4 will explain through a real world experiment on a testbench how they can be used to monitor gear degradation . 2. GEAR SIGNAL MODELISATION A gear is composed o f : a wheel # 1, with N, teeth, rotating at speed F 1, a wheel # 2, with N, teeth, rotating at speed F2. The gear frequency Feng is defined as .the frequency where teeth come into contact with each other : Feng = NI * Fl = N2 * F2 = 1 / Teng. Let us assume that a flaw has appeared on one of the two wheels (typically this flaw is a tooth flaking). This will result in an amplitude modulation combined with a phase modulation [ 11. Hence the vibratory signal produced by the gear rotation can be modeled as follows : sd(t) = p p ( l +op(t))cos(2npF,, +@, + b p ( r ) ) P where : ap(t) = Z A L sin(2niFrt +ai P ) + P J ) P rotation frequency of the faulty wheel. gear frequency . amplitude modulation of the pm harmonic of FmP. phase modulation of the pm harmonic of Fm,. In section 2 we will establish a model for the gear signals before choosing a technique to analyse them. Section 3 introduces the cyclostationarity and the spectral","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130697364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Array Calibration Utilizing Clutter Scattering","authors":"F. Robey, D. Fuhrmann, S. I. Krich","doi":"10.1109/SSAP.1994.572507","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572507","url":null,"abstract":"A method of calibrating airborne radar arrays using scattering from the surface of the earth is proposed. The method uses the mapping of clutter azimuth and elevation to Doppler frequency to separate the energy that is arriving from different azimuths and elevations. Calibration is then accomplished using the clutter data. Range and Doppler ambiguities are examined to predict the adverse impact these will have on the resulting calibration. The necessarily limited data collection time duration limits the resolution of the clutter scattering to some angular extent: the impact of this resolution on the calibration results are also examined.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132834381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Source Number Estimator In Pulse Train Data","authors":"J. Perkins","doi":"10.1109/SSAP.1994.572442","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572442","url":null,"abstract":"Data that consists of a number of superimposed pulse trains (each train having a simple pulse arrival pattern) is considered. The number of independent trains present is of interest in a number of applications. By considering the variance (over window position) of the number of pulses lying in a time window the number of periodic sources can be determined. This method of Cox and Smith is generalized from strictly periodic emitters to those with noise or periodic patterns.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114923177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast Subspace Tracking","authors":"D. Rabideau, A. Steinhardt","doi":"10.1109/SSAP.1994.572516","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572516","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114961647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral Modifications Due to Particular Clock Changes","authors":"B. Lacaze","doi":"10.1109/SSAP.1994.572502","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572502","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134026148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Linear Estimation Of Moments For (almost) Cyclostationary Signals","authors":"Chen-Yuan Lo, H. Lev-Ari","doi":"10.1109/SSAP.1994.572424","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572424","url":null,"abstract":"The behavior of linear estimators of moments in a periodic or almost periodic non-stationary environment is analyzed. The performance of such estimators is evaluated in terms of their time-averaged variance and time-averaged squared bias. Optimal estimators that minimize a convex combination of bias and variance are derived. The superiority of such optimally-weighted averaging over the conventional (exponentially-windowed) moment estimation technique is demonstrated by means of a simple example. The same example also serves to illustrate the difficulties encountered when the construction of such optimal estimators relies on uncertain parametric information, as well as to demonstrate the feasibility of overcoming such difficulties by using appropriately designed (robust) optimal estimators.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121873498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Direction Estimation in the Presence of Spatially Correlated Noise","authors":"B. Goransson","doi":"10.1109/SSAP.1994.572468","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572468","url":null,"abstract":"In most algorithms for direction estimation of signal wavefronts, the additive noise term is assumed to be spatially white or known to within a multiplicative scalar. Since the surrounding environment and orientation of the array may be time varying, the requirement of known noise statistics is seldom satisfied in practice. At high signal-to-noise ratio (SNR) the deviation from these assumption are not critical. However, at low SNR, the degradation may be severe. By introducing a banded structure noise model, it is possible to estimate the noise covariance simultaneously as the direction parameters are estimated. This technique considerably reduces the bias on the direction estimates, that are induced by the colored noise.In this paper such a parameterization is proposed, and the asymptotic bias is investigated with respect to small perturbations in the noise model.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125806992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selecting Statistical Information In Set Theoretic Signal Processing","authors":"P. L. Combettes, T. Chaussalet","doi":"10.1109/SSAP.1994.572433","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572433","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125998229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}