Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572504
P. Musumeci
{"title":"Estimation of Signal Parameters for Optimal Array Filters","authors":"P. Musumeci","doi":"10.1109/SSAP.1994.572504","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572504","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"45 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":"125364476","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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572526
D. Linebarger, R.D. DeGroat, E. Dowling
{"title":"Computational Reductions Exploiting Structure in Forward/backward Data","authors":"D. Linebarger, R.D. DeGroat, E. Dowling","doi":"10.1109/SSAP.1994.572526","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572526","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"63 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":"124702408","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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572540
C. Nockemann, G. Tillack, C. Bellon, K. Johannsen, S. Heine
{"title":"How to Treat the Reliability of Defect Detection and Assessment in Nondestructive Testing?","authors":"C. Nockemann, G. Tillack, C. Bellon, K. Johannsen, S. Heine","doi":"10.1109/SSAP.1994.572540","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572540","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"12 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":"124913559","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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572430
D. Abraham
In the detection of unknown deterministic signals in noise, consideration may be restricted to statistics that are sufficient for detection of certain classes of signals. Here, the case of correlated statistics that are assumed to have analytically intractable probability distributions is considered. A locally optimal quantized detector that merges the multivariate sufficient statistics is proposed. Quantization is required for implementation, which utilizes a Monte-Carlo evaluation of the levels minimizing the mean squared error for a specific partitioning of the range space of the sufficient statistics. Performance improvement over the individual statistics and a test using the maximum of the individual statistics is illustrated with an example.
{"title":"A Monte-Carlo Method for Locally Optimal Quantized Merging of Correlated Detection Statistics","authors":"D. Abraham","doi":"10.1109/SSAP.1994.572430","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572430","url":null,"abstract":"In the detection of unknown deterministic signals in noise, consideration may be restricted to statistics that are sufficient for detection of certain classes of signals. Here, the case of correlated statistics that are assumed to have analytically intractable probability distributions is considered. A locally optimal quantized detector that merges the multivariate sufficient statistics is proposed. Quantization is required for implementation, which utilizes a Monte-Carlo evaluation of the levels minimizing the mean squared error for a specific partitioning of the range space of the sufficient statistics. Performance improvement over the individual statistics and a test using the maximum of the individual statistics is illustrated with an example.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"10 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":"124922762","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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572438
H. Messer, P. M. Schultheiss
An important signal parameter estimation problem is time-delay estimation. Here the unknown is the time origin of the signal: s (l, θ) = s (l − θ). The duration of the signal (the domain over which the signal is de ned) is assumed brief compared with the observation interval L. Although in continuous time the signal delay is a continuous-valued variable, in discrete time it is not. Consequently, the maximum likelihood estimate cannot be found by di erentiation, and we must determine the maximum likelihood estimate of signal delay by the most fundamental expression of the maximization procedure. Assuming Gaussian noise, the maximum likelihood estimate of delay is the solution of
信号参数估计的一个重要问题是时延估计。这里的未知数是信号的时间原点:s (l， θ) = s (l−θ)。与观测区间l相比，假设信号的持续时间(信号被定义的域)较短。虽然在连续时间中信号延迟是一个连续值变量，但在离散时间中它不是。因此，不能用微分法求最大似然估计，必须用最大化过程的最基本表达式来确定信号延迟的最大似然估计。假设高斯噪声，时延的最大似然估计是
{"title":"On Time Delay Estimation","authors":"H. Messer, P. M. Schultheiss","doi":"10.1109/SSAP.1994.572438","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572438","url":null,"abstract":"An important signal parameter estimation problem is time-delay estimation. Here the unknown is the time origin of the signal: s (l, θ) = s (l − θ). The duration of the signal (the domain over which the signal is de ned) is assumed brief compared with the observation interval L. Although in continuous time the signal delay is a continuous-valued variable, in discrete time it is not. Consequently, the maximum likelihood estimate cannot be found by di erentiation, and we must determine the maximum likelihood estimate of signal delay by the most fundamental expression of the maximization procedure. Assuming Gaussian noise, the maximum likelihood estimate of delay is the solution of","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"36 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":"125507657","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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572425
V. Nagesha, S. Kay
Statistical inference for vector time series having a mixed spectral representation is considered. The approach is to use a finite-parameter model and compute the maximum likelihood estimates of the underlying descriptors. Statistically/computationally efficient implementations are studied.
{"title":"Estimation of Multichannel Mixed Spectra","authors":"V. Nagesha, S. Kay","doi":"10.1109/SSAP.1994.572425","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572425","url":null,"abstract":"Statistical inference for vector time series having a mixed spectral representation is considered. The approach is to use a finite-parameter model and compute the maximum likelihood estimates of the underlying descriptors. Statistically/computationally efficient implementations are studied.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"155 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":"132262765","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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572418
P. L. Combettes
{"title":"Set Theoretic Signal Processing","authors":"P. L. Combettes","doi":"10.1109/SSAP.1994.572418","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572418","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"23 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":"132776186","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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572479
Chien-Chung Hsiao, Chong-Yung Chi
This paper presents image modeling and restoration by higher-order statistics based 2-D inverse filters. A given original image +(m, n) is processed by an optimum inverse filter v(m, n) which is designed by maximizing cumulant based criteria J r ,m = ICmlr/ICrlm where r is even, m > T 2 2 and C,,, (Cr) denotes mthorder (rth-order cumulant of the output e(m,n) of be modeled as the output of a linear shift-invariant (LSI) system h(m, n) driven by e(m, n) where h(m, n) is a stable inverse filter of v(m,n) . When a blurred image y(m,n) = t(m,n) * g(m,n,) rather than the original image z(m, n) is given, t (m, n) can be restored by first estimating e (m,n) using the previous inverse filter criteria and then obtain t(m,n) = e(m, n) * h(m, n). Some experimental results are provided to support the proposed image modeling and restoration method. the 2-D inverse A Iter. The original image z (m, n) can
{"title":"Image Modeling And Restoration By Higher-order Statistics Based Inverse Filters","authors":"Chien-Chung Hsiao, Chong-Yung Chi","doi":"10.1109/SSAP.1994.572479","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572479","url":null,"abstract":"This paper presents image modeling and restoration by higher-order statistics based 2-D inverse filters. A given original image +(m, n) is processed by an optimum inverse filter v(m, n) which is designed by maximizing cumulant based criteria J r ,m = ICmlr/ICrlm where r is even, m > T 2 2 and C,,, (Cr) denotes mthorder (rth-order cumulant of the output e(m,n) of be modeled as the output of a linear shift-invariant (LSI) system h(m, n) driven by e(m, n) where h(m, n) is a stable inverse filter of v(m,n) . When a blurred image y(m,n) = t(m,n) * g(m,n,) rather than the original image z(m, n) is given, t (m, n) can be restored by first estimating e (m,n) using the previous inverse filter criteria and then obtain t(m,n) = e(m, n) * h(m, n). Some experimental results are provided to support the proposed image modeling and restoration method. the 2-D inverse A Iter. The original image z (m, n) can","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"77 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":"126816942","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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572457
B. Hochwald, A. Nehorai
We present a bound on the number of sources identifiable in a class of array processing models with multiple parameters and signals per source. The bound is applied to determine the maximum number of uniquely resolvable plane-wave sources in various acoustic and electromagnetic vector-sensor models. We examine the use of a priori information about the sources, the effects of known and unknown noise characteristics, and the presence of nuisance parameters. Connections between identifiability and existence of the Cramer-Rao bound (CRB) are investigated. We show quantitatively how assumptions about the parameters can fundamentally affect the maximum number of identifiable sources.
{"title":"Identifiability in Array Processing Models with Vector-Sensor Applications","authors":"B. Hochwald, A. Nehorai","doi":"10.1109/SSAP.1994.572457","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572457","url":null,"abstract":"We present a bound on the number of sources identifiable in a class of array processing models with multiple parameters and signals per source. The bound is applied to determine the maximum number of uniquely resolvable plane-wave sources in various acoustic and electromagnetic vector-sensor models. We examine the use of a priori information about the sources, the effects of known and unknown noise characteristics, and the presence of nuisance parameters. Connections between identifiability and existence of the Cramer-Rao bound (CRB) are investigated. We show quantitatively how assumptions about the parameters can fundamentally affect the maximum number of identifiable sources.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"67 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":"124450168","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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572524
R. Kirlin, A. Trzynadlowski
This paper presents several new results and ideas. First the harmonic spectrum and power spectral density (noise density) of random width modulation for minimum loss vector PWM is analyzed and plots for one case of modulation and randomization parameters are given. As a result of the analysis of this modulation scheme, a novel time-domain formulation (autocorrelation) of the spectral information is presented. In this form we find all of the necessary details for understanding the mechanisms of general randomization schemes for suppressing harmonics and converting harmonic power to the noise spectral density. The insights found in the autocorrelation expressions allow at least one optimum design of the randomization parameters of any PWM method. The proposed optimization is but one of many indicated and implied by our methods. Introduction Research on random pulse width modulation (RPWM) techniques for static power converters, mainly three -phase inverters, has recently gained momentum. Initiated by our paper [l] in 1987, in 1992 alone the studies on various WWM issues were reported in over a dozen publications [2]. The RPWM techniques have been found to significantly improve the noise and vibration characteristics of converter-fed motors in adjustable speed drive systems [3,4]. Figure 1 shows both deterministic and random switching rate or random width P W M signals for producing ac from a dc source. The deterministic pattern is often calculated to maximize fundamental power while sometimes nulling or minimizing some selected harmonics. However all deterministic modulations have exactly the same switching patterns in all periods of the fundamental. This naturally leads to harmonics. The basic principle of RPWM consists in introduction of a random factor to the switching patterns of the controlled converter. With regard to three-phase inverters, each cycle of the output voltage is generated by a different randomized combination of pulses of the a(t) deterministic PWM T I
{"title":"Spectral Design of Randomized Pulse Width Modulation in DC to AC Converters","authors":"R. Kirlin, A. Trzynadlowski","doi":"10.1109/SSAP.1994.572524","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572524","url":null,"abstract":"This paper presents several new results and ideas. First the harmonic spectrum and power spectral density (noise density) of random width modulation for minimum loss vector PWM is analyzed and plots for one case of modulation and randomization parameters are given. As a result of the analysis of this modulation scheme, a novel time-domain formulation (autocorrelation) of the spectral information is presented. In this form we find all of the necessary details for understanding the mechanisms of general randomization schemes for suppressing harmonics and converting harmonic power to the noise spectral density. The insights found in the autocorrelation expressions allow at least one optimum design of the randomization parameters of any PWM method. The proposed optimization is but one of many indicated and implied by our methods. Introduction Research on random pulse width modulation (RPWM) techniques for static power converters, mainly three -phase inverters, has recently gained momentum. Initiated by our paper [l] in 1987, in 1992 alone the studies on various WWM issues were reported in over a dozen publications [2]. The RPWM techniques have been found to significantly improve the noise and vibration characteristics of converter-fed motors in adjustable speed drive systems [3,4]. Figure 1 shows both deterministic and random switching rate or random width P W M signals for producing ac from a dc source. The deterministic pattern is often calculated to maximize fundamental power while sometimes nulling or minimizing some selected harmonics. However all deterministic modulations have exactly the same switching patterns in all periods of the fundamental. This naturally leads to harmonics. The basic principle of RPWM consists in introduction of a random factor to the switching patterns of the controlled converter. With regard to three-phase inverters, each cycle of the output voltage is generated by a different randomized combination of pulses of the a(t) deterministic PWM T I","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"265 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":"123269588","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}
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