Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572501
B. L. Scala, R. Bitmead, B. G. Quinn
The problem of constructing a frequency tracker for weak, narrowband signals with slowly varying frequency is considered. An extended Kalman filter is proposed that uses prior knowledge of the nature of the signal to overcome the difficulties presented by the inherent nonlinearity of the problem and the very low signal-to-noise ratios.
{"title":"An Extended Kalman Filter Frequency Tracker for High-Noise Environments","authors":"B. L. Scala, R. Bitmead, B. G. Quinn","doi":"10.1109/SSAP.1994.572501","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572501","url":null,"abstract":"The problem of constructing a frequency tracker for weak, narrowband signals with slowly varying frequency is considered. An extended Kalman filter is proposed that uses prior knowledge of the nature of the signal to overcome the difficulties presented by the inherent nonlinearity of the problem and the very low signal-to-noise ratios.","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":"115009656","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.1994.572498
S. Anderson, A. Nehorai
We present a model for polarized seismic waves where the data are collected by three-component geophone receivers. The model is based on two parameters describing the polarization properties of the waveforms. These parameters are the ellipticity and the orientation angle of the polarization ellipse. The model describes longitudinal waveforms (P-waves) as well as elliptically polarized waves. For the latter waves the direction-of-propagation of the waveform is in the plane spanned by the ellipse's major and minor axes; Rayleigh waves are treated as a special case. We analyze the identifiability of the models and derive the Cramer-Rao and mean-square-angular-error (MSAE) bounds involving one or two three-component geophones.
{"title":"Analysis of a Polarized Seismic Wave Model","authors":"S. Anderson, A. Nehorai","doi":"10.1109/SSAP.1994.572498","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572498","url":null,"abstract":"We present a model for polarized seismic waves where the data are collected by three-component geophone receivers. The model is based on two parameters describing the polarization properties of the waveforms. These parameters are the ellipticity and the orientation angle of the polarization ellipse. The model describes longitudinal waveforms (P-waves) as well as elliptically polarized waves. For the latter waves the direction-of-propagation of the waveform is in the plane spanned by the ellipse's major and minor axes; Rayleigh waves are treated as a special case. We analyze the identifiability of the models and derive the Cramer-Rao and mean-square-angular-error (MSAE) bounds involving one or two three-component geophones.","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":"114902062","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.1994.572449
A. Bassias
In this paper the advantage provided by the fourth order cumulant domain is exploited, that is the suppression of additive Gaussian noise sources. Thus we examine the effect of combining the transformation matrices of the Coherent Signal Subspace Method with spatial fourth order cumulant matrices for the estimation of the direction of arrival of non Gaussian wideband signals in spatially correlated, Gaussian noise of unknown covariance. Instead of spatial covariance matrices which are used by the CSS Method, the transformation matices are used here in order to align the Signal Subspaces of the fourth order cumulant matrices at the temporal frequencies in which each snapshot of the array outputs is decomposed, with the Signal Subspace at center frequency. It is shown with simulations that the new method can suppress noise and resolve signals in cases where the spatial covariance based methods do not but the estimates present higher bias and strong fluctuation. INTRODUCTION The problem of estimating the angles of arrival of non Gaussian wideband signals in spatially correlated Gaussian noises of unknown correlation matrix, using an array of !.ensorS. is addressed. This is a situation that is often encountered in practice where a deviation of the signals as stochastic processes from being Gaussian is observed and the noise correlation matrix is not spatially white as is required by the signal subspace based methods in order to give reasonable estimates. The Coherent Signal Subspace (CSS) method [ 13 has been developed for the estimation of diredon of arrival of wideband signals received by an anay of sensors. Here, the effect of combining the Transformation matrices of the CSS methods with fourth order cumulant matrices is examined. This is motivated by the known property of Gaussian processes that all cumulant spectra of order greater than two are identical to ;en, [2]. So, by using the fourth order cumulants of the array data, any additive Gaussian noises corrupting non Gaussian signals will (in principle) be suppressed. Array processing methods for narrowband signals in spatially correlated Gaussian noise have been introduced in [3]. There, the processing is performed in the time domain ithile, all the processing here is performed in the 'rcquency domain. In the following, the CSS method is reviewed briefly and the new method is explained in detail. Its performance is assessed with simulations and a short discussion with comments and observations is provided. PROBLEM FORMULATION A wavefield generated by M wideband sources in the presence of noise is sampled temporally and spatially by a passive array of N (N>M) hydrophones with a known arbitrary geometry. The source signals are characterized as zero mean, non Gaussian stationary stochastic processes over the observation interval To, bandlimited to a common frequency band with bandwidth B which may be of the same order of magnitude as the center frequency fo. The source signal vector s(t) is defin
{"title":"DOA Estimation Using Coherent Signal - Subspace Method Based On Fourth - Order Cumulants","authors":"A. Bassias","doi":"10.1109/SSAP.1994.572449","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572449","url":null,"abstract":"In this paper the advantage provided by the fourth order cumulant domain is exploited, that is the suppression of additive Gaussian noise sources. Thus we examine the effect of combining the transformation matrices of the Coherent Signal Subspace Method with spatial fourth order cumulant matrices for the estimation of the direction of arrival of non Gaussian wideband signals in spatially correlated, Gaussian noise of unknown covariance. Instead of spatial covariance matrices which are used by the CSS Method, the transformation matices are used here in order to align the Signal Subspaces of the fourth order cumulant matrices at the temporal frequencies in which each snapshot of the array outputs is decomposed, with the Signal Subspace at center frequency. It is shown with simulations that the new method can suppress noise and resolve signals in cases where the spatial covariance based methods do not but the estimates present higher bias and strong fluctuation. INTRODUCTION The problem of estimating the angles of arrival of non Gaussian wideband signals in spatially correlated Gaussian noises of unknown correlation matrix, using an array of !.ensorS. is addressed. This is a situation that is often encountered in practice where a deviation of the signals as stochastic processes from being Gaussian is observed and the noise correlation matrix is not spatially white as is required by the signal subspace based methods in order to give reasonable estimates. The Coherent Signal Subspace (CSS) method [ 13 has been developed for the estimation of diredon of arrival of wideband signals received by an anay of sensors. Here, the effect of combining the Transformation matrices of the CSS methods with fourth order cumulant matrices is examined. This is motivated by the known property of Gaussian processes that all cumulant spectra of order greater than two are identical to ;en, [2]. So, by using the fourth order cumulants of the array data, any additive Gaussian noises corrupting non Gaussian signals will (in principle) be suppressed. Array processing methods for narrowband signals in spatially correlated Gaussian noise have been introduced in [3]. There, the processing is performed in the time domain ithile, all the processing here is performed in the 'rcquency domain. In the following, the CSS method is reviewed briefly and the new method is explained in detail. Its performance is assessed with simulations and a short discussion with comments and observations is provided. PROBLEM FORMULATION A wavefield generated by M wideband sources in the presence of noise is sampled temporally and spatially by a passive array of N (N>M) hydrophones with a known arbitrary geometry. The source signals are characterized as zero mean, non Gaussian stationary stochastic processes over the observation interval To, bandlimited to a common frequency band with bandwidth B which may be of the same order of magnitude as the center frequency fo. The source signal vector s(t) is defin","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":"129102196","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.1994.572493
V. Pierson, N. Martin
{"title":"Comparison of Shape Descriptors for Feature Extraction of a Time- Frequency Image","authors":"V. Pierson, N. Martin","doi":"10.1109/SSAP.1994.572493","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572493","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":"121568362","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.1994.572459
T. Lo, H. Leun, J. Litval
In this letter, we compare the direction-ofarrival (DOA) technique based on the use of a radial basis function (RBF) network with the standard MUSIC algorithm. The RBF network is used to approximate the functional relationship between sensor outputs and the directionof-arrivals. Simulation results show that the new technique has a better performance, in terms of estimation errors.
{"title":"A Comparative Study of Statistical and Neural DOA Estimation Techniques","authors":"T. Lo, H. Leun, J. Litval","doi":"10.1109/SSAP.1994.572459","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572459","url":null,"abstract":"In this letter, we compare the direction-ofarrival (DOA) technique based on the use of a radial basis function (RBF) network with the standard MUSIC algorithm. The RBF network is used to approximate the functional relationship between sensor outputs and the directionof-arrivals. Simulation results show that the new technique has a better performance, in terms of estimation errors.","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":"113933648","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.1994.572503
D. Marshall
In airborne radar systems, both jamming and ground clutter interference can be suppressed by coherently processing multiple time samples of the array output using adaptive interference nulling techniques. This space-time nulling problem may present special difficulties with regard to the availability of sufficient samples for adaptive training. The subject of this paper is an algorithm which nulls jamming and then clutter in two separate stages. The clutter is nulled in a signal subspace of reduced dimension. This approach makes the best use of the available training data, and maintains nulling degrees of freedom where they are most needed. Performance is illustrated with data obtained from Lincoln Laboratory's Mountaintop radar system.
{"title":"A Two Step Adaptive Interference Nulling Algorithm For Use With Airborne Sensor Arrays","authors":"D. Marshall","doi":"10.1109/SSAP.1994.572503","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572503","url":null,"abstract":"In airborne radar systems, both jamming and ground clutter interference can be suppressed by coherently processing multiple time samples of the array output using adaptive interference nulling techniques. This space-time nulling problem may present special difficulties with regard to the availability of sufficient samples for adaptive training. The subject of this paper is an algorithm which nulls jamming and then clutter in two separate stages. The clutter is nulled in a signal subspace of reduced dimension. This approach makes the best use of the available training data, and maintains nulling degrees of freedom where they are most needed. Performance is illustrated with data obtained from Lincoln Laboratory's Mountaintop radar system.","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":"131947777","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.1994.572539
Fu Li, H. Xiao, Yibing Guo, Jin Yang
Multipath fading is one of the major practical concerns in wireless communications. Multipath problem always exists in mobile environment, especially for mobile unit which is often embedded in its surroundings. A time-variant tapped line delay model has been used for multipath fading in a wide-band spread spectrum mobile system. In this paper, we proposed to use the detection and estimation techniques developed in spectrum analysis and array processing to determine the number of delay paths (or taps), to estimate the time delay of each path, and to estimate tap weight of each delay path based on chip rate channel information in a realistic mobile environment. Simulations show that the new approach outperforms the existing approaches.
{"title":"On Modeling of a Mobile Multipath Fading Channel","authors":"Fu Li, H. Xiao, Yibing Guo, Jin Yang","doi":"10.1109/SSAP.1994.572539","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572539","url":null,"abstract":"Multipath fading is one of the major practical concerns in wireless communications. Multipath problem always exists in mobile environment, especially for mobile unit which is often embedded in its surroundings. A time-variant tapped line delay model has been used for multipath fading in a wide-band spread spectrum mobile system. In this paper, we proposed to use the detection and estimation techniques developed in spectrum analysis and array processing to determine the number of delay paths (or taps), to estimate the time delay of each path, and to estimate tap weight of each delay path based on chip rate channel information in a realistic mobile environment. Simulations show that the new approach outperforms the existing approaches.","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":"132894363","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.1994.572538
M. A. Koerber, D. Fuhrmann
This paper presents a method for est,imating an airhorne antenna array’s spatial response pattern, or array manifold, using radar clutter as a source of calibration data. Doppler processing is used to isolate returns in range and azimuth bins: this da ta is then used in conjunction with a low-order Fourier series model t o estimate the response pattern. The computational problem which results is one referred to as Least Squares with Data Scaling (LSDS), whose solution makes possible the elimination of ambiguity between incident field strength and antenna element gain, to within a single complex constant. The prior knowledge of the element location can be used to improve calibration accuracy and significantly reduce the required Fourier series model order.
{"title":"Radar Antenna Calibration Using Range-Doppler Data","authors":"M. A. Koerber, D. Fuhrmann","doi":"10.1109/SSAP.1994.572538","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572538","url":null,"abstract":"This paper presents a method for est,imating an airhorne antenna array’s spatial response pattern, or array manifold, using radar clutter as a source of calibration data. Doppler processing is used to isolate returns in range and azimuth bins: this da ta is then used in conjunction with a low-order Fourier series model t o estimate the response pattern. The computational problem which results is one referred to as Least Squares with Data Scaling (LSDS), whose solution makes possible the elimination of ambiguity between incident field strength and antenna element gain, to within a single complex constant. The prior knowledge of the element location can be used to improve calibration accuracy and significantly reduce the required Fourier series model order.","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":"133150150","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.1994.572480
R. Kumaresan, C. S. Ramalingam, A. Rao
We propose an improved version of an estimator-predictor filter bank, originally proposed by Costas [l], for decomposing and tracking multiple, nonstationary sinusoidal components present in a signal. Each component is assigned a signal estimator which is a causal filter, and a predictor. The estimator-predictor combination estimates the next time-sample of its signal component, which is then subtracted from the composite input signal. Ideally, no signal component will then interfere with accurate estimation of the others. However, Costas’s predictor performs poorly when there are components with rapidly changing envelopes. In this paper, we propose an improved predictor that compensates for the group delay introduced in the signal components by the causal filtering, by minimizing a prediction error criterion. With this improved predictor, using a computer synthesized multicomponent signal, we show that we achieve cleaner separation of signal components when compared with Costas’s method. We also show that this method can be used to separate the essentially harmonic partials in voiced speech.
{"title":"RISC: An Improved Costas Estimator-Predictor Filter Bank For Decomposing Multicomponent Signals","authors":"R. Kumaresan, C. S. Ramalingam, A. Rao","doi":"10.1109/SSAP.1994.572480","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572480","url":null,"abstract":"We propose an improved version of an estimator-predictor filter bank, originally proposed by Costas [l], for decomposing and tracking multiple, nonstationary sinusoidal components present in a signal. Each component is assigned a signal estimator which is a causal filter, and a predictor. The estimator-predictor combination estimates the next time-sample of its signal component, which is then subtracted from the composite input signal. Ideally, no signal component will then interfere with accurate estimation of the others. However, Costas’s predictor performs poorly when there are components with rapidly changing envelopes. In this paper, we propose an improved predictor that compensates for the group delay introduced in the signal components by the causal filtering, by minimizing a prediction error criterion. With this improved predictor, using a computer synthesized multicomponent signal, we show that we achieve cleaner separation of signal components when compared with Costas’s method. We also show that this method can be used to separate the essentially harmonic partials in voiced speech.","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":"128881233","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.1994.572440
W. Padgett, D. Williams
As the complexity of a detection algorithm increases, analytic performance evaluation becomes increasingly difficult and is often intractable. In such cases, Monte Carlo sunulations can be used, but they often require an excessive amount of computation. As a means of reducing this computation, importance sampling has been applied with great success to simulations of digital communications receivers. In this paper, importance sampling strategies for the simulation of random signal detectors are presented. These strategies are shown to provide considerable computational savings over conventional Monte Carlo simulations. Additionally, simplicity and ease of use are emphasized in the development of these strategies.
{"title":"Efficient Simulation Of Random Signal Detectors","authors":"W. Padgett, D. Williams","doi":"10.1109/SSAP.1994.572440","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572440","url":null,"abstract":"As the complexity of a detection algorithm increases, analytic performance evaluation becomes increasingly difficult and is often intractable. In such cases, Monte Carlo sunulations can be used, but they often require an excessive amount of computation. As a means of reducing this computation, importance sampling has been applied with great success to simulations of digital communications receivers. In this paper, importance sampling strategies for the simulation of random signal detectors are presented. These strategies are shown to provide considerable computational savings over conventional Monte Carlo simulations. Additionally, simplicity and ease of use are emphasized in the development of these strategies.","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":"127388304","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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