Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572472
R. Kirlin, B. Hedstrom, S. Subramanian, D. Shpak
This paper deals with DOA estimation techniques used for tracking a mixed layer float. Cross correlation followed by additional filtering gives us satisfactory delay estimates. These delay estimates are then suitably weighted to obtain a DOA estimate whose tracking is enhanced by the use of Kalman filtering. To improve the resolution of this preliminary DOA estimation and to provide versatility, high resolution subspace-based algorithms are used. The high resolution techniques for this problem require time-shifting to overcome the problem of widely spaced sensors.
{"title":"Robust Array Processing for DOA Estimation With Widely Spaced Sensors","authors":"R. Kirlin, B. Hedstrom, S. Subramanian, D. Shpak","doi":"10.1109/SSAP.1994.572472","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572472","url":null,"abstract":"This paper deals with DOA estimation techniques used for tracking a mixed layer float. Cross correlation followed by additional filtering gives us satisfactory delay estimates. These delay estimates are then suitably weighted to obtain a DOA estimate whose tracking is enhanced by the use of Kalman filtering. To improve the resolution of this preliminary DOA estimation and to provide versatility, high resolution subspace-based algorithms are used. The high resolution techniques for this problem require time-shifting to overcome the problem of widely spaced sensors.","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":"126541544","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.1994.572481
A. Pagès-Zamora, M. Lagunas
This paper deals with the problem of modelling and identifying non-linear systems (NLSs). Although the final architecture applies to NLSs with memory, it is also important to bear in mind that the proposed filter consists in a generalisation of a previous scheme which was designed to model memoryless N U S . In consequence, the simplicity of the memoryless filter is useful to intuitively understand the final architecture and how it can be improved when dealing with specific problems. On the other hand, the filter with memory shows the complexity and computational load that non-linear theory usually involves. But at the same time. it allows a large number of different possibilities depending on the concrete application and it also opens new ways of future work.
{"title":"The K-filter: A New Model Of Non-linear Systems With Memory","authors":"A. Pagès-Zamora, M. Lagunas","doi":"10.1109/SSAP.1994.572481","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572481","url":null,"abstract":"This paper deals with the problem of modelling and identifying non-linear systems (NLSs). Although the final architecture applies to NLSs with memory, it is also important to bear in mind that the proposed filter consists in a generalisation of a previous scheme which was designed to model memoryless N U S . In consequence, the simplicity of the memoryless filter is useful to intuitively understand the final architecture and how it can be improved when dealing with specific problems. On the other hand, the filter with memory shows the complexity and computational load that non-linear theory usually involves. But at the same time. it allows a large number of different possibilities depending on the concrete application and it also opens new ways of future work.","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":"125302866","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.1994.572529
B. Quach, Ho-fung Leung, T. Lo, J. Litva
This paper discusses a neurobeamformer based on the Hopfield neural network which is used to suppress narrowband interference in spread spectrum communications. In this application, we considered a liiear array consisting of four spatially separated elements. The received spread spectrum signal is bi-phase modulated by a PN (Pseudo-Noise) code. This code has parameter which will prove to be well suited for use in network Comparators to obtain optimal antenna array pattem, i.e., the main beam is steered towards the desired signal while nulls are directed towards the interference. The proposed Hopfield beamformer can be characterized as a constrained quadratic function. It uses random, asynchronous updates in order to provide real time response to rapid time-varying environments. The constrained quadratic programmed neural network has an associate energy function which the network always seeks to minimize, which leads to optimization of the array weights. In this paper we present simulations carried out with a Hopfield beamformer. It will be shown that its performance is better than that of a LMS beamformer.
{"title":"Hopfield Network Approach to Beamforrning in Spread Spectrum Communication","authors":"B. Quach, Ho-fung Leung, T. Lo, J. Litva","doi":"10.1109/SSAP.1994.572529","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572529","url":null,"abstract":"This paper discusses a neurobeamformer based on the Hopfield neural network which is used to suppress narrowband interference in spread spectrum communications. In this application, we considered a liiear array consisting of four spatially separated elements. The received spread spectrum signal is bi-phase modulated by a PN (Pseudo-Noise) code. This code has parameter which will prove to be well suited for use in network Comparators to obtain optimal antenna array pattem, i.e., the main beam is steered towards the desired signal while nulls are directed towards the interference. The proposed Hopfield beamformer can be characterized as a constrained quadratic function. It uses random, asynchronous updates in order to provide real time response to rapid time-varying environments. The constrained quadratic programmed neural network has an associate energy function which the network always seeks to minimize, which leads to optimization of the array weights. In this paper we present simulations carried out with a Hopfield beamformer. It will be shown that its performance is better than that of a LMS beamformer.","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":"113933458","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.1994.572517
J. Tourneret, B. Lacaze
Some sets of parameters, obtained from a one-to-one transformation from AR ones, have successfully been used in Spectral Analysis and Classification. In a previous paper El], a statistical analysis of the cepstral coefficient probability density function (p.d.f.) revealed why, in many cases, the k-NN rule is a necessary tool in this particular representation space. The aim of this paper is to study a further set of parameters : the reflection coefficients.
{"title":"Study of the Couple (Reflection Coefficient, K-Nn Rule)","authors":"J. Tourneret, B. Lacaze","doi":"10.1109/SSAP.1994.572517","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572517","url":null,"abstract":"Some sets of parameters, obtained from a one-to-one transformation from AR ones, have successfully been used in Spectral Analysis and Classification. In a previous paper El], a statistical analysis of the cepstral coefficient probability density function (p.d.f.) revealed why, in many cases, the k-NN rule is a necessary tool in this particular representation space. The aim of this paper is to study a further set of parameters : the reflection coefficients.","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":"114361008","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.1994.572477
V. Capdevielle, C. Servière, J. Lacoume
{"title":"Application Of Source Separation To Wide Band Signals","authors":"V. Capdevielle, C. Servière, J. Lacoume","doi":"10.1109/SSAP.1994.572477","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572477","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":"123016993","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.1994.572489
C. Detka, P. Loughlin, A. El-Jaroudi
We review several methods for combining time-dependent spectral estimates that result from different evolutionary spectral models. Examples are presented to illustrate the value of each combination method. In addition, we explore the effect of noise on these combination methods.
{"title":"On Combining Evolutionary Spectral Estimates","authors":"C. Detka, P. Loughlin, A. El-Jaroudi","doi":"10.1109/SSAP.1994.572489","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572489","url":null,"abstract":"We review several methods for combining time-dependent spectral estimates that result from different evolutionary spectral models. Examples are presented to illustrate the value of each combination method. In addition, we explore the effect of noise on these combination methods.","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":"122435495","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.1994.572475
M. Lagunas
Since the publication in 1957 of the work of Andrei Kolmogorov 181 in mapping a function of multiple variables by means functions of a single variable, many mathematicians and engineers try , with different degree of success and not without controversy 1191. to find the direct application of it to multiple extremes problems, rooting of multivariate polynomials, neural networks and pattern recognition. This paper revisits the theorem from the optic of a generalised architecture for signal processing 1281. It is envisaged the high potential of the theorem to handle either linear or non-linear processing problems. A specific implementation following the main guide-lines of the theorem is reported, as well as some preliminary results concerning the design, implementation and performance of non-linear systems. The applications cover non linear transmission channels for communications, instantaneous companders and prediction of chaotic series.
{"title":"The Kolmogorov Mapping Theorem In Signal Processing","authors":"M. Lagunas","doi":"10.1109/SSAP.1994.572475","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572475","url":null,"abstract":"Since the publication in 1957 of the work of Andrei \u0000Kolmogorov 181 in mapping a function of multiple variables \u0000by means functions of a single variable, many \u0000mathematicians and engineers try , with different degree of \u0000success and not without controversy 1191. to find the direct \u0000application of it to multiple extremes problems, rooting of \u0000multivariate polynomials, neural networks and pattern \u0000recognition. This paper revisits the theorem from the optic \u0000of a generalised architecture for signal processing 1281. It is \u0000envisaged the high potential of the theorem to handle either \u0000linear or non-linear processing problems. A specific \u0000implementation following the main guide-lines of the \u0000theorem is reported, as well as some preliminary results \u0000concerning the design, implementation and performance of \u0000non-linear systems. The applications cover non linear \u0000transmission channels for communications, instantaneous \u0000companders and prediction of chaotic series.","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":"129867952","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.1994.572446
A. Sesay
The method proposed is one that is based on the descrepancy of the means under different hypotheses. A hypotheses test is constructed in terms of conditional innovations sequences generated from the received sequence. Observing that the only statistics that change with hypotheses are the conditional means, the problem is treated as a test of the descrepancy of the means. An approximate sequential test is used and the test statistic is shown to be Frasersufficient. Asymptotic probability of error results are obtained using Cramer's theorem for maximum likelihood estimates.
{"title":"Mean-based Tests And Aymptotic Performance For Digital Communications","authors":"A. Sesay","doi":"10.1109/SSAP.1994.572446","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572446","url":null,"abstract":"The method proposed is one that is based on the descrepancy of the means under different hypotheses. A hypotheses test is constructed in terms of conditional innovations sequences generated from the received sequence. Observing that the only statistics that change with hypotheses are the conditional means, the problem is treated as a test of the descrepancy of the means. An approximate sequential test is used and the test statistic is shown to be Frasersufficient. Asymptotic probability of error results are obtained using Cramer's theorem for maximum likelihood estimates.","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":"124458832","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.1994.572478
J. Cardoso, É. Moulines
We consider the use of 2nd-and 4th-order cumulants for estimating the direction-of-arrival (DOAs) in narrow band array processing. The Fisher information about the DOAs contained in several cumulant statistics is computed. Numerical evaluation of the related Cram er-Rao bound is then used to point out, in this limited study, some advantages and drawbacks of using higher-order statistics.
{"title":"How Much More DOA Information In Higher-order Statistics ?","authors":"J. Cardoso, É. Moulines","doi":"10.1109/SSAP.1994.572478","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572478","url":null,"abstract":"We consider the use of 2nd-and 4th-order cumulants for estimating the direction-of-arrival (DOAs) in narrow band array processing. The Fisher information about the DOAs contained in several cumulant statistics is computed. Numerical evaluation of the related Cram er-Rao bound is then used to point out, in this limited study, some advantages and drawbacks of using higher-order statistics.","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":"121268597","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.1994.572506
Hung Nguyen, Harry L Van Trees, I. Center
For a single signal, numerical comparison of the Bayesian Cramer-Rao lower bound, the Chazan-Ziv-Zakai lower bound and the Weiss-Weinstein lower bound on DOA estimation MSE shows that the CZZLB is the tightest lower bound and can accurately predict the threshold SNR, which is a critical system design parameter. The analysis is extended to two signals where the multiple parameter WWLB is applicable but appears to be a weak lower bound. Simulation results show that below the threshold SNR, a maximum likelihood DOA estimation procedure such as the Expectation Maximization(EM) algorithm seeded by the Alternating Projection Maximization (APM) algorithm should be used to provide the best DOA estimation performance. Above the threshold SNR, the use of the statistically efficient MUSIC algorithm is adequate.
{"title":"Comparison of Performance Bounds for Doa Estimation","authors":"Hung Nguyen, Harry L Van Trees, I. Center","doi":"10.1109/SSAP.1994.572506","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572506","url":null,"abstract":"For a single signal, numerical comparison of the Bayesian Cramer-Rao lower bound, the Chazan-Ziv-Zakai lower bound and the Weiss-Weinstein lower bound on DOA estimation MSE shows that the CZZLB is the tightest lower bound and can accurately predict the threshold SNR, which is a critical system design parameter. The analysis is extended to two signals where the multiple parameter WWLB is applicable but appears to be a weak lower bound. Simulation results show that below the threshold SNR, a maximum likelihood DOA estimation procedure such as the Expectation Maximization(EM) algorithm seeded by the Alternating Projection Maximization (APM) algorithm should be used to provide the best DOA estimation performance. Above the threshold SNR, the use of the statistically efficient MUSIC algorithm is adequate.","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":"116498026","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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