Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572436
I. Kirsteins
An analysis and interpretation of the reduced-rank generalized likelihood-ratio test (RR-GLRT) detector is presented in this paper. First, simple and accurate approximations to the RR-GLRT test statistics are derived. The approximations are verified by computer simulation and are shown to be accurate over a wide range of interference and signal levels These approximation are then used to show that the RR-GLRT is related to the UMP invariant test and to calculate the moments and deflection of the RR-GLRT statistic.
{"title":"Analysis and Interpretation of the Reduced-Rank Generalized Likelihood-Ratio Test","authors":"I. Kirsteins","doi":"10.1109/SSAP.1994.572436","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572436","url":null,"abstract":"An analysis and interpretation of the reduced-rank generalized likelihood-ratio test (RR-GLRT) detector is presented in this paper. First, simple and accurate approximations to the RR-GLRT test statistics are derived. The approximations are verified by computer simulation and are shown to be accurate over a wide range of interference and signal levels These approximation are then used to show that the RR-GLRT is related to the UMP invariant test and to calculate the moments and deflection of the RR-GLRT statistic.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114836786","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.1994.572522
M. Fargues, R. Hippenstiel
We recently investigated various spectral-based classification schemes designed to separate wideband transient signals and compared their performances to those obtained using a back-propagation neural network implementation [2]. The spectral-based measures considered include the Bhattacharyya distance, the divergence, the normalized cross-correlation coefficient, and the modified normalized cross-correlation coefficient. Results showed that accurate classification may be obtained using spectral-based measures and that the performances compare, or are sometimes better, to those obtained using neural networks when the training data used to train the neural network is small. In this paper we investigate the robustness of the spectral measures and the neural network approximation classification schemes to white additive noise degradation in the testing sets. Results show that the spectral-based techniques are more robust when the testing sets are degraded with noise.
{"title":"Robust Spectral-Based Techniques for Classification of Wldeband Transient Signals","authors":"M. Fargues, R. Hippenstiel","doi":"10.1109/SSAP.1994.572522","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572522","url":null,"abstract":"We recently investigated various spectral-based classification schemes designed to separate wideband transient signals and compared their performances to those obtained using a back-propagation neural network implementation [2]. The spectral-based measures considered include the Bhattacharyya distance, the divergence, the normalized cross-correlation coefficient, and the modified normalized cross-correlation coefficient. Results showed that accurate classification may be obtained using spectral-based measures and that the performances compare, or are sometimes better, to those obtained using neural networks when the training data used to train the neural network is small. In this paper we investigate the robustness of the spectral measures and the neural network approximation classification schemes to white additive noise degradation in the testing sets. Results show that the spectral-based techniques are more robust when the testing sets are degraded with noise.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116744134","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.1994.572486
Huang-Lin Yang, Chong-Yung Chi
This paper presents a new cumulant based phase estimation method for linear time-invariant (LTI) systems with only non-Gaussian measurements contaminated by Gaussian noise. An optimum allpass filter is designed to process the given measurements such that its output has a maximum Mth-order (2 3) cumulant in absolute value. It can be shown that the system phase is equivalent to the negative value of the optimum allpass filter phase except for a linear phase factor. Some simulation results are provided to support the proposed phase estimation method.
{"title":"A New Cumulant Based Phase Estimation Nonminimum-phase Systems By Allpass","authors":"Huang-Lin Yang, Chong-Yung Chi","doi":"10.1109/SSAP.1994.572486","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572486","url":null,"abstract":"This paper presents a new cumulant based phase estimation method for linear time-invariant (LTI) systems with only non-Gaussian measurements contaminated by Gaussian noise. An optimum allpass filter is designed to process the given measurements such that its output has a maximum Mth-order (2 3) cumulant in absolute value. It can be shown that the system phase is equivalent to the negative value of the optimum allpass filter phase except for a linear phase factor. Some simulation results are provided to support the proposed phase estimation method.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114215427","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.1994.572534
F. Claveau, S. Lord, D. Gingras, P. Fortier
A laser-based contactless displacement measurement system developed at the National Optics Institute is used for data acquisition to analyze the mechanical vibrations exhibited by vibrating structures and machines. The analysis of these vibrations requires a number of signal processing operations which include the determination of the system conditions through a classification of various observed vibration signatures and the detection of changes in the vibration signature in order to identify possible trends. This information is also combined with the physical characteristics and contextual data (operating mode, etc.) of the system under surveillance to allow the evaluation of certain characteristics like fatigue, abnormal stress, life span, etc., resulting in a high level classification of mechanical behaviours and structural faults according to the type of application. The aim of this paper is to introduce the problem, the instrumentation, and the requirements in terms of statistical signal processing.
{"title":"Mechanical Vibration Analysis Using an Optical Sensor","authors":"F. Claveau, S. Lord, D. Gingras, P. Fortier","doi":"10.1109/SSAP.1994.572534","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572534","url":null,"abstract":"A laser-based contactless displacement measurement system developed at the National Optics Institute is used for data acquisition to analyze the mechanical vibrations exhibited by vibrating structures and machines. The analysis of these vibrations requires a number of signal processing operations which include the determination of the system conditions through a classification of various observed vibration signatures and the detection of changes in the vibration signature in order to identify possible trends. This information is also combined with the physical characteristics and contextual data (operating mode, etc.) of the system under surveillance to allow the evaluation of certain characteristics like fatigue, abnormal stress, life span, etc., resulting in a high level classification of mechanical behaviours and structural faults according to the type of application. The aim of this paper is to introduce the problem, the instrumentation, and the requirements in terms of statistical signal processing.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126528228","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.1994.572543
J. Ward, A. Steinhardt
Advanced airborne radars must perform target detection in the presence of interference and heavy clutter, Space-time adaptive processing (STAP) refers to a class of adaptive filtering techniques that simultaneously rocess the spatial signals from an antenna .array and d e temporal signals from multiple pulses an order to suppress both jammin and clutter. A reduceddimension suboptimum STh' architecture utilizing multi le dop ler filter banks on each element is suggestel Digrent methods for choosing the doppler filters are considered and a condition which yields minimum clutter r a d is derived. PRI-staggered postdop ler meets the condition and rovides both ezcellent per6rmance with few degrees orfreedom and the abili t y to maintain low adapted doppler sadelobes. Adjacent bin post-doppler re uires more de rees of freedom when low doppler sidelo%es are desire!
{"title":"Multiwindow Post-Doppler Space-Time Adaptive Processing","authors":"J. Ward, A. Steinhardt","doi":"10.1109/SSAP.1994.572543","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572543","url":null,"abstract":"Advanced airborne radars must perform target detection in the presence of interference and heavy clutter, Space-time adaptive processing (STAP) refers to a class of adaptive filtering techniques that simultaneously rocess the spatial signals from an antenna .array and d e temporal signals from multiple pulses an order to suppress both jammin and clutter. A reduceddimension suboptimum STh' architecture utilizing multi le dop ler filter banks on each element is suggestel Digrent methods for choosing the doppler filters are considered and a condition which yields minimum clutter r a d is derived. PRI-staggered postdop ler meets the condition and rovides both ezcellent per6rmance with few degrees orfreedom and the abili t y to maintain low adapted doppler sadelobes. Adjacent bin post-doppler re uires more de rees of freedom when low doppler sidelo%es are desire!","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125628763","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.1994.572471
R. DeLap, A. Hero
In this paper we develop an adaptive beamsummer for direction of arrival (DOA) estimation of slow fading Raleigh signals, using a new design approach, termed "Adaptive Detection/Estimation for specific Tasks" (ADEPT). For DOA estimation, the ADEPT method yields a weight adaptation criterion which is optimized for those weights that minimize the Cramer-Rao (CR) lower bound on achievable mean-square-error of any unbiased DOA estimator constructed on the beamsummer outputs. Simulation results are provided which show that the ADEPT DOA beamsummer yields DOA estimates whose meansquared error (MSE) approaches that of the more complex maximum likelihood implementation.
{"title":"Adaptive Beamforming for Slow Fading Rayleigh Signals","authors":"R. DeLap, A. Hero","doi":"10.1109/SSAP.1994.572471","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572471","url":null,"abstract":"In this paper we develop an adaptive beamsummer for direction of arrival (DOA) estimation of slow fading Raleigh signals, using a new design approach, termed \"Adaptive Detection/Estimation for specific Tasks\" (ADEPT). For DOA estimation, the ADEPT method yields a weight adaptation criterion which is optimized for those weights that minimize the Cramer-Rao (CR) lower bound on achievable mean-square-error of any unbiased DOA estimator constructed on the beamsummer outputs. Simulation results are provided which show that the ADEPT DOA beamsummer yields DOA estimates whose meansquared error (MSE) approaches that of the more complex maximum likelihood implementation.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123064792","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.1994.572483
J. M. Salavedra, E. Masgrau, A. Moreno, J. Estarellas
We study some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim-Oppenheim [2], where the AR spectral estimation of the speech is carried out using a 2nd-order analysis. But in our algorithms we consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors, providing a different frame length where AR estimation is done. Information of previous speech frames is used to initiate speech AR modelling of the current frame. Two parameters are introduced to dessign Wiener filter at first iteration of this iterative algorithm. These parameters are the Interframe Factor IF and the Previous Frame Iteration PFI. They allow a very important noise suppression after processing only fxst iteration of this algorithm, without any appreciable increase of distortion.
{"title":"Variable Frame Length Of A Higher Order Speech AR Estimation In A Speech Enhancement System","authors":"J. M. Salavedra, E. Masgrau, A. Moreno, J. Estarellas","doi":"10.1109/SSAP.1994.572483","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572483","url":null,"abstract":"We study some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim-Oppenheim [2], where the AR spectral estimation of the speech is carried out using a 2nd-order analysis. But in our algorithms we consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors, providing a different frame length where AR estimation is done. Information of previous speech frames is used to initiate speech AR modelling of the current frame. Two parameters are introduced to dessign Wiener filter at first iteration of this iterative algorithm. These parameters are the Interframe Factor IF and the Previous Frame Iteration PFI. They allow a very important noise suppression after processing only fxst iteration of this algorithm, without any appreciable increase of distortion.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134434928","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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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