Pub Date : 1993-11-01DOI: 10.1109/ACSSC.1993.342396
D. Carhoun
Reduced-rank subspace projection methods are used indirectly in frequency and angle-of-arrival estimation algorithms such as MUSIC and its relatives, but they are not commonly used directly in least-squares detection applications. The author has been exploring their use for the processing of underwater acoustic receiver array data for detection and matched-field localization. He describes and illustrates several techniques that have been developed and applied to signals recorded from different types of arrays.<>
{"title":"Signal subspace projection methods of adaptive sensor array processing","authors":"D. Carhoun","doi":"10.1109/ACSSC.1993.342396","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342396","url":null,"abstract":"Reduced-rank subspace projection methods are used indirectly in frequency and angle-of-arrival estimation algorithms such as MUSIC and its relatives, but they are not commonly used directly in least-squares detection applications. The author has been exploring their use for the processing of underwater acoustic receiver array data for detection and matched-field localization. He describes and illustrates several techniques that have been developed and applied to signals recorded from different types of arrays.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134623891","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 : 1993-11-01DOI: 10.1109/ACSSC.1993.342500
T. A. Barton, D. Fuhrmann
Under a complex-Gaussian data model, a maximum likelihood method based on the iterative expectation-maximization algorithm is given to estimate structured covariance matrices for multidimensional data organized into column-vector form. The covariance structures of interest involve a hierarchy of subblocks within the covariance matrix, and include block-circulant and block Toeplitz matrices and their generalizations. These covariance matrices are elements of certain covariance constraint sets such that each element may be described as a matrix multiplication of a known matrix of Kronecker products and a nonnegative-definite, block-diagonal matrix. Several convergence properties of the estimation procedure are discussed, and an example of algorithm behavior is provided.<>
{"title":"Covariance estimation for multidimensional data using the EM algorithm","authors":"T. A. Barton, D. Fuhrmann","doi":"10.1109/ACSSC.1993.342500","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342500","url":null,"abstract":"Under a complex-Gaussian data model, a maximum likelihood method based on the iterative expectation-maximization algorithm is given to estimate structured covariance matrices for multidimensional data organized into column-vector form. The covariance structures of interest involve a hierarchy of subblocks within the covariance matrix, and include block-circulant and block Toeplitz matrices and their generalizations. These covariance matrices are elements of certain covariance constraint sets such that each element may be described as a matrix multiplication of a known matrix of Kronecker products and a nonnegative-definite, block-diagonal matrix. Several convergence properties of the estimation procedure are discussed, and an example of algorithm behavior is provided.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133879025","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 : 1993-11-01DOI: 10.1109/ACSSC.1993.342592
J. Shynk, R. Gooch
The multistage CMA adaptive beamformer is capable of separating multiple narrowband sources without pilot or training signals. It is comprised of a cascade of CM (constant modulus) array subsections, each of which captures one of the signals impinging an the array. An adaptive signal canceller follows each CM array to remove captured signals from the input before processing by subsequent sections. Based an a stochastic analysis, we derive the steady-state convergence properties of the system, including its direction-finding capabilities. For mutually uncorrelated sources and noise, it is shown that the canceller exactly removes a captured signal, thereby reducing the rank of the effective array matrix of the next subsection by one.<>
{"title":"Convergence properties of the multistage CMA adaptive beamformer","authors":"J. Shynk, R. Gooch","doi":"10.1109/ACSSC.1993.342592","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342592","url":null,"abstract":"The multistage CMA adaptive beamformer is capable of separating multiple narrowband sources without pilot or training signals. It is comprised of a cascade of CM (constant modulus) array subsections, each of which captures one of the signals impinging an the array. An adaptive signal canceller follows each CM array to remove captured signals from the input before processing by subsequent sections. Based an a stochastic analysis, we derive the steady-state convergence properties of the system, including its direction-finding capabilities. For mutually uncorrelated sources and noise, it is shown that the canceller exactly removes a captured signal, thereby reducing the rank of the effective array matrix of the next subsection by one.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125706883","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 : 1993-11-01DOI: 10.1109/ACSSC.1993.342571
C. Wei, D. Cochran
This paper presents methods for constructing bandlimited wavelets whose time-shifted and dyadically dilated replicates form orthonormal sets. The wavelets sets constructed have desirable spectral spreading properties when used as symbols for encoding digital communication signals, as has been proposed in recent literature.<>
{"title":"Bandlimited orthogonal wavelet symbols","authors":"C. Wei, D. Cochran","doi":"10.1109/ACSSC.1993.342571","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342571","url":null,"abstract":"This paper presents methods for constructing bandlimited wavelets whose time-shifted and dyadically dilated replicates form orthonormal sets. The wavelets sets constructed have desirable spectral spreading properties when used as symbols for encoding digital communication signals, as has been proposed in recent literature.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132405614","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 : 1993-11-01DOI: 10.1109/ACSSC.1993.342472
M. Khansari, Tsuhan Chen
The commutativity of multidimensional downsamplers and upsamplers have been discussed very intensively for the past few years. This is due to its important applications in sampling structure conversion, e.g., the conversion between conventional television signals and high definition television (HDTV) signals. Among many other results, one useful test for such commutativity was found to be that the two matrices which define the multidimensional downsampling and upsampling should be commutative and coprime. However, the problem of finding multidimensional downsamplers and upsamplers that satisfy these conditions has remained open. In this paper, we develop a systematic procedure to solve this open problem.<>
{"title":"On commutativity of multidimensional downsamplers and upsamplers","authors":"M. Khansari, Tsuhan Chen","doi":"10.1109/ACSSC.1993.342472","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342472","url":null,"abstract":"The commutativity of multidimensional downsamplers and upsamplers have been discussed very intensively for the past few years. This is due to its important applications in sampling structure conversion, e.g., the conversion between conventional television signals and high definition television (HDTV) signals. Among many other results, one useful test for such commutativity was found to be that the two matrices which define the multidimensional downsampling and upsampling should be commutative and coprime. However, the problem of finding multidimensional downsamplers and upsamplers that satisfy these conditions has remained open. In this paper, we develop a systematic procedure to solve this open problem.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134478188","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 : 1993-11-01DOI: 10.1109/ACSSC.1993.342604
S. R. Pillai
A new higher order generalization of magnitude and power complementary filters is proposed. The proposed scheme is shown to have superior frequency characteristics compared to the ordinary complementary filters. Applications of these generalized complementary filters include subband coding for audio and video, and sharpening of amplitude characteristics of digital filters. Interestingly, as shown in the present paper, this new design procedure can be used to generate ordinary multichannel magnitude and power complementary filters with sharper band responses.<>
{"title":"Design of generalized higher order complementary filters","authors":"S. R. Pillai","doi":"10.1109/ACSSC.1993.342604","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342604","url":null,"abstract":"A new higher order generalization of magnitude and power complementary filters is proposed. The proposed scheme is shown to have superior frequency characteristics compared to the ordinary complementary filters. Applications of these generalized complementary filters include subband coding for audio and video, and sharpening of amplitude characteristics of digital filters. Interestingly, as shown in the present paper, this new design procedure can be used to generate ordinary multichannel magnitude and power complementary filters with sharper band responses.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117286371","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 : 1993-11-01DOI: 10.1109/ACSSC.1993.342507
O. Adeyemi, S. Kadambe, G. Boudreaux-Bartels
The paper describes a simple method for detecting a class of first order or low dimensional discrete-time signals whose dynamical properties have the form of the logistic map. The authors have developed a maximum likelihood estimation (MLE) technique for the tuning parameter a and the initial condition of a logistic map embedded in zero-mean white Gaussian noise. They use the estimated values to determine the chaotic behavior of class of first order discrete time signals. They have also derived the recursive Cramer Rao lower bound (CRLB) for the tuning parameter a and the initial condition to characterize the performance of the developed estimators.<>
{"title":"The detection and characterization of a class of discrete time chaotic signals","authors":"O. Adeyemi, S. Kadambe, G. Boudreaux-Bartels","doi":"10.1109/ACSSC.1993.342507","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342507","url":null,"abstract":"The paper describes a simple method for detecting a class of first order or low dimensional discrete-time signals whose dynamical properties have the form of the logistic map. The authors have developed a maximum likelihood estimation (MLE) technique for the tuning parameter a and the initial condition of a logistic map embedded in zero-mean white Gaussian noise. They use the estimated values to determine the chaotic behavior of class of first order discrete time signals. They have also derived the recursive Cramer Rao lower bound (CRLB) for the tuning parameter a and the initial condition to characterize the performance of the developed estimators.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117219488","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 : 1993-11-01DOI: 10.1109/ACSSC.1993.342513
W. Blair, D. Kazakos
In many multisensor systems, the number of sensors and the type of sensors supporting a particular target track can vary with time due to the mobility, type, and resource limitations of the individual sensors. This variability in the configuration of the sensor system poses a significant problem when tracking maneuvering targets because of the uncertainty in the target motion model. When the sensor system is fixed, the uncertainty in the motion model is addressed in the design of the tracking algorithm by considering individual target trajectories. However, considering individual target trajectories in conjunction with every possible multisensor configuration is not practical. In the paper, the problem of tracking maneuvering targets with multiple intermittent sensors is illustrated through an example involving a single motion model example. The interacting multiple model (IMM) algorithm is applied to this problem and simulation results are given.<>
{"title":"Tracking maneuvering targets with multiple, intermittent sensors","authors":"W. Blair, D. Kazakos","doi":"10.1109/ACSSC.1993.342513","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342513","url":null,"abstract":"In many multisensor systems, the number of sensors and the type of sensors supporting a particular target track can vary with time due to the mobility, type, and resource limitations of the individual sensors. This variability in the configuration of the sensor system poses a significant problem when tracking maneuvering targets because of the uncertainty in the target motion model. When the sensor system is fixed, the uncertainty in the motion model is addressed in the design of the tracking algorithm by considering individual target trajectories. However, considering individual target trajectories in conjunction with every possible multisensor configuration is not practical. In the paper, the problem of tracking maneuvering targets with multiple intermittent sensors is illustrated through an example involving a single motion model example. The interacting multiple model (IMM) algorithm is applied to this problem and simulation results are given.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116079627","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 : 1993-11-01DOI: 10.1109/ACSSC.1993.342319
Wenyuan Xu, M. Kaveh
Angle-dependent weighted MUSIC or weighted norm MUSIC is a broad class of MUSIC-like parameter estimators which includes as special case the standard "spectral" MUSIC. Based on a general approach for deriving the point statistics of the signal-subspace estimators, the relation between the large-sample moments of MUSIC and angle-dependent weighted MUSIC is presented in this paper. The optimum weight function resulting in the estimator with zero bias of order N/sup -1/ is derived. The approximate realizations of this optimum estimator in a parametric subclass of angle-dependent weighted MUSIC for arrays measuring closely spaced sources are discussed. Simulation examples verify the theoretical analysis and demonstrate the proposed estimators have small estimation biases over a wide range of signal-to-noise ratio.<>
{"title":"The optimum weight of angle-dependent weighted MUSIC and its approximations","authors":"Wenyuan Xu, M. Kaveh","doi":"10.1109/ACSSC.1993.342319","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342319","url":null,"abstract":"Angle-dependent weighted MUSIC or weighted norm MUSIC is a broad class of MUSIC-like parameter estimators which includes as special case the standard \"spectral\" MUSIC. Based on a general approach for deriving the point statistics of the signal-subspace estimators, the relation between the large-sample moments of MUSIC and angle-dependent weighted MUSIC is presented in this paper. The optimum weight function resulting in the estimator with zero bias of order N/sup -1/ is derived. The approximate realizations of this optimum estimator in a parametric subclass of angle-dependent weighted MUSIC for arrays measuring closely spaced sources are discussed. Simulation examples verify the theoretical analysis and demonstrate the proposed estimators have small estimation biases over a wide range of signal-to-noise ratio.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115488442","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 : 1993-11-01DOI: 10.1109/ACSSC.1993.342607
A. Jaffer, W. Jones
In many signal processing applications, the need arises for the design of complex coefficient finite impulse response (FIR) filters to meet the specifications which cannot be approximated by real coefficient FIR filters. The paper presents a new technique for the design of complex FIR filters based on minimizing a weighted integral squared-error criterion subject to the constraint that the resulting filter response be affine phase (i.e., generalize linear phase), The technique makes use of the necessary and sufficient conditions for a causal complex FIR filter to possess affine phase which are explicitly derived in the present paper. The method is non-iterative and computationally efficient. Several illustrative filter design examples are presented with excellent results.<>
{"title":"Constrained least-squares design and characterization of affine phase complex FIR filters","authors":"A. Jaffer, W. Jones","doi":"10.1109/ACSSC.1993.342607","DOIUrl":"https://doi.org/10.1109/ACSSC.1993.342607","url":null,"abstract":"In many signal processing applications, the need arises for the design of complex coefficient finite impulse response (FIR) filters to meet the specifications which cannot be approximated by real coefficient FIR filters. The paper presents a new technique for the design of complex FIR filters based on minimizing a weighted integral squared-error criterion subject to the constraint that the resulting filter response be affine phase (i.e., generalize linear phase), The technique makes use of the necessary and sufficient conditions for a causal complex FIR filter to possess affine phase which are explicitly derived in the present paper. The method is non-iterative and computationally efficient. Several illustrative filter design examples are presented with excellent results.<<ETX>>","PeriodicalId":266447,"journal":{"name":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116036406","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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