Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246791
J. Jonsson, A. Steinhardt
The authors view the interference as a stochastic process n(m,t) in two variables. They assume a uniform linear array that the carrier frequency is known and that the direction of arrival of the desired signal does not change. The approach is an application/extension of the multi-window method for spectrum estimation and harmonic analysis introduced by Thomson (Proc. IEEE vol.70, no.9, p.1055-96, Sept.1982). How the signal is estimated if present, and how to estimate the DOA, are discussed briefly.<>
{"title":"Detection and recovery of a narrow-band signal in severely nonstationary noise","authors":"J. Jonsson, A. Steinhardt","doi":"10.1109/SSAP.1992.246791","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246791","url":null,"abstract":"The authors view the interference as a stochastic process n(m,t) in two variables. They assume a uniform linear array that the carrier frequency is known and that the direction of arrival of the desired signal does not change. The approach is an application/extension of the multi-window method for spectrum estimation and harmonic analysis introduced by Thomson (Proc. IEEE vol.70, no.9, p.1055-96, Sept.1982). How the signal is estimated if present, and how to estimate the DOA, are discussed briefly.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127132298","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 : 1992-10-07DOI: 10.1109/SSAP.1992.246799
G. Brown, J. H. McClellan, E. J. Holder
An eigenstructure approach is presented for array processing with imperfect sensors. Each sensor is modeled as having an unknown angularly dependent phase error with ideal gain. The proposed technique is capable of compensating for sensor location error, unknown sensor phase patterns, and various types of environmental inhomogeneities. The algorithm can be used in self-calibration applications since the sensor perturbations are estimated in addition to angle of arrival of the sources. Based on the method of homotopy continuation it will find all solutions for the minimum. Since the algorithm is computationally intensive, a sub-optimal less intensive approach is also provided.<>
{"title":"A homotopy continuation approach for self-calibration of arrays with general phase perturbations","authors":"G. Brown, J. H. McClellan, E. J. Holder","doi":"10.1109/SSAP.1992.246799","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246799","url":null,"abstract":"An eigenstructure approach is presented for array processing with imperfect sensors. Each sensor is modeled as having an unknown angularly dependent phase error with ideal gain. The proposed technique is capable of compensating for sensor location error, unknown sensor phase patterns, and various types of environmental inhomogeneities. The algorithm can be used in self-calibration applications since the sensor perturbations are estimated in addition to angle of arrival of the sources. Based on the method of homotopy continuation it will find all solutions for the minimum. Since the algorithm is computationally intensive, a sub-optimal less intensive approach is also provided.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"55 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124865704","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 : 1992-10-07DOI: 10.1109/SSAP.1992.246814
G. R. Wilson, K. Baugh
This paper considers specifically the sum of coherently related narrowband signals of unknown frequencies and amplitudes. It employs the method of non-stationary higher order spectra (NSHOS) and develops a detection statistic based on the non-stationary bispectrum. Within the domain that excludes the stationary HOS, the NSHOS detection statistics as asymptotically zero for any type of stationary colored Gaussian or non-Gaussian noise, while within the domain of the stationary HOS they are zero for any type of stationary colored Gaussian or symmetric non-Gaussian noise. The detection performance is compared analytically and experimentally with the more traditional power spectrum method based on a periodogram detection statistic for Gaussian and nonGaussian noise.<>
{"title":"Non-parametric detection of a class of cyclo-stationary signals in stationary colored non-Gaussian noise using non-stationary higher order spectra","authors":"G. R. Wilson, K. Baugh","doi":"10.1109/SSAP.1992.246814","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246814","url":null,"abstract":"This paper considers specifically the sum of coherently related narrowband signals of unknown frequencies and amplitudes. It employs the method of non-stationary higher order spectra (NSHOS) and develops a detection statistic based on the non-stationary bispectrum. Within the domain that excludes the stationary HOS, the NSHOS detection statistics as asymptotically zero for any type of stationary colored Gaussian or non-Gaussian noise, while within the domain of the stationary HOS they are zero for any type of stationary colored Gaussian or symmetric non-Gaussian noise. The detection performance is compared analytically and experimentally with the more traditional power spectrum method based on a periodogram detection statistic for Gaussian and nonGaussian noise.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125148206","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 : 1992-10-07DOI: 10.1109/SSAP.1992.246778
S. Sivanand
The coherent signal-subspace approach to broadband adaptive beamforming involves a focusing preprocessor that aligns signal spaces at different frequencies to a common one by means of transformations and a narrowband beamformer following the preprocessor. The merits of the approach are decorrelation of multipath signals and partial adaptivity due to single-frequency weights. This paper deals with the optimality of the focused beamformer compared with that of conventional broadband beamformer. An analytical and empirical evaluation of different focusing transformations is presented.<>
{"title":"On focusing preprocessor for broadband beamforming","authors":"S. Sivanand","doi":"10.1109/SSAP.1992.246778","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246778","url":null,"abstract":"The coherent signal-subspace approach to broadband adaptive beamforming involves a focusing preprocessor that aligns signal spaces at different frequencies to a common one by means of transformations and a narrowband beamformer following the preprocessor. The merits of the approach are decorrelation of multipath signals and partial adaptivity due to single-frequency weights. This paper deals with the optimality of the focused beamformer compared with that of conventional broadband beamformer. An analytical and empirical evaluation of different focusing transformations is presented.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122051835","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 : 1992-10-07DOI: 10.1109/SSAP.1992.246880
M. Vishwanath, R. Owens, M. Irwin
A number of lower bounds on the communication and multiplicative complexity are derived. The (Area)*(Time)/sup 2/ (AT/sup 2/) bound for the discrete short time Fourier transform, the discrete Wigner-Ville distribution, the discrete ambiguity function and the discrete Gabor transform is shown to be AT/sup 2/= Omega (N/sup 3/ log/sup 2/ N), where N/sup 2/ is the number of output points. The lower bound on multiplicative complexity for these is shown to be Omega (N/sup 2/). For the N-point discrete wavelet transform a lower bound of AT/sup 2/= Omega (N/sup 2/ log/sup 2/ N) and a multiplicative complexity of Omega (N) are the same as the lower bounds for the DFT.<>
{"title":"The computational complexity of time-frequency distributions","authors":"M. Vishwanath, R. Owens, M. Irwin","doi":"10.1109/SSAP.1992.246880","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246880","url":null,"abstract":"A number of lower bounds on the communication and multiplicative complexity are derived. The (Area)*(Time)/sup 2/ (AT/sup 2/) bound for the discrete short time Fourier transform, the discrete Wigner-Ville distribution, the discrete ambiguity function and the discrete Gabor transform is shown to be AT/sup 2/= Omega (N/sup 3/ log/sup 2/ N), where N/sup 2/ is the number of output points. The lower bound on multiplicative complexity for these is shown to be Omega (N/sup 2/). For the N-point discrete wavelet transform a lower bound of AT/sup 2/= Omega (N/sup 2/ log/sup 2/ N) and a multiplicative complexity of Omega (N) are the same as the lower bounds for the DFT.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125558914","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 : 1992-10-07DOI: 10.1109/SSAP.1992.246893
J. Parish
This paper describes the calculation of Lyapunov exponents and fractal dimension for a chaotic time series. These metrics provide appropriate characterization for the nonperiodic signals generated by low dimensional nonlinear systems. Nonlinear dynamics account for the underlying ergodicity of the nonlinear system which produces such signals. Results for several nonlinear systems are presented.<>
{"title":"Nonlinear dynamics techniques in signal processing","authors":"J. Parish","doi":"10.1109/SSAP.1992.246893","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246893","url":null,"abstract":"This paper describes the calculation of Lyapunov exponents and fractal dimension for a chaotic time series. These metrics provide appropriate characterization for the nonperiodic signals generated by low dimensional nonlinear systems. Nonlinear dynamics account for the underlying ergodicity of the nonlinear system which produces such signals. Results for several nonlinear systems are presented.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120948258","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 : 1992-10-07DOI: 10.1109/SSAP.1992.246874
S. D. Silverstein
A new algorithm is based upon division of the eigenstructure of the sample covariance into approximate signal and noise subspaces due to fluctuations caused by finite data samples. These fluctuation effects are calculated using stochastic perturbation theoretic techniques. These results show that the moments of the MUSIC null spectrum can be approximated by linear functionals of the source SNR and the number of snapshots. All theoretical predictions are in accord with the simulation results. A featured simulation demonstrates accurate source power estimates for three sources separated by sub-Rayleigh resolution spatial frequencies with a weak source of 0 dB SNR sandwiched between two much larger sources of 40 and 20 dB.<>
{"title":"A fundamental algorithm for the power estimation of closely separated spectral sources","authors":"S. D. Silverstein","doi":"10.1109/SSAP.1992.246874","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246874","url":null,"abstract":"A new algorithm is based upon division of the eigenstructure of the sample covariance into approximate signal and noise subspaces due to fluctuations caused by finite data samples. These fluctuation effects are calculated using stochastic perturbation theoretic techniques. These results show that the moments of the MUSIC null spectrum can be approximated by linear functionals of the source SNR and the number of snapshots. All theoretical predictions are in accord with the simulation results. A featured simulation demonstrates accurate source power estimates for three sources separated by sub-Rayleigh resolution spatial frequencies with a weak source of 0 dB SNR sandwiched between two much larger sources of 40 and 20 dB.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"565 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116455303","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 : 1992-10-07DOI: 10.1109/SSAP.1992.246827
Chong-Yung Chi, Wu-Ton Chen
This criterion requires only partial Mth-order cumulants C/sub M,e/(0,k/sub 1/, k/sub 1/, . . ., k/sub M/2-1/, k/sub M/ /sub /2-1/) of the prediction error e(k) where M is even. Theoretically, it is shown that the proposed filter associated with a stationary process x(k) is the same as the conventional correlation based (minimum-phase) LPE filter associated with the nonGaussian signal y(k) (noise-free). Simulation results show that when y(k) is an autoregressive process of known order, the proposed filter works well.<>
{"title":"Linear prediction based on higher order statistics by a new criterion","authors":"Chong-Yung Chi, Wu-Ton Chen","doi":"10.1109/SSAP.1992.246827","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246827","url":null,"abstract":"This criterion requires only partial Mth-order cumulants C/sub M,e/(0,k/sub 1/, k/sub 1/, . . ., k/sub M/2-1/, k/sub M/ /sub /2-1/) of the prediction error e(k) where M is even. Theoretically, it is shown that the proposed filter associated with a stationary process x(k) is the same as the conventional correlation based (minimum-phase) LPE filter associated with the nonGaussian signal y(k) (noise-free). Simulation results show that when y(k) is an autoregressive process of known order, the proposed filter works well.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"21 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113967838","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 : 1992-10-07DOI: 10.1109/SSAP.1992.246872
I. Gorodnitsky, B. Rao
A general class of linear inverse problems in which the solutions are sparse and localized is considered. The proposed algorithm is a nonparametric approach that finds sparse and localized solutions without prior information on the constraints. Each step of the iterative procedure consists in solving a weighted least squares problem wherein the weighting matrix is determined by the solution from the previous iteration. Some properties of the algorithm along with its applications to problems in direction of arrival and spectrum estimation are presented.<>
{"title":"A new iterative weighted norm minimization algorithm and its applications","authors":"I. Gorodnitsky, B. Rao","doi":"10.1109/SSAP.1992.246872","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246872","url":null,"abstract":"A general class of linear inverse problems in which the solutions are sparse and localized is considered. The proposed algorithm is a nonparametric approach that finds sparse and localized solutions without prior information on the constraints. Each step of the iterative procedure consists in solving a weighted least squares problem wherein the weighting matrix is determined by the solution from the previous iteration. Some properties of the algorithm along with its applications to problems in direction of arrival and spectrum estimation are presented.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115072798","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 : 1992-10-07DOI: 10.1109/SSAP.1992.246815
C. Spooner, W. Gardner
The cumulant theory of cyclostationary time-series is applied to several types of problems that arise in the area of signal interception and to the problem of estimating the relative time-delay of a heavily corrupted signal received at two locations. The theory characterizes the additive sine waves present in the output of nonlinear transformations of such time-series. The detection and time-delay estimation problems posed are difficult to solve because the signal is weak, the noise and interference are nonstationary and non-Gaussian, and the signal does not exhibit second-order cyclostationarity.<>
{"title":"Exploitation of higher-order cyclostationarity for weak-signal detection and time-delay estimation","authors":"C. Spooner, W. Gardner","doi":"10.1109/SSAP.1992.246815","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246815","url":null,"abstract":"The cumulant theory of cyclostationary time-series is applied to several types of problems that arise in the area of signal interception and to the problem of estimating the relative time-delay of a heavily corrupted signal received at two locations. The theory characterizes the additive sine waves present in the output of nonlinear transformations of such time-series. The detection and time-delay estimation problems posed are difficult to solve because the signal is weak, the noise and interference are nonstationary and non-Gaussian, and the signal does not exhibit second-order cyclostationarity.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128505451","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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