The Experts below are selected from a list of 3630 Experts worldwide ranked by ideXlab platform
Petre Stoica - One of the best experts on this subject based on the ideXlab platform.
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review of user parameter free robust adaptive beamforming algorithms
Asilomar Conference on Signals Systems and Computers, 2008Co-Authors: Tarik Yardibi, Petre StoicaAbstract:This paper provides a comprehensive review of user parameter-free robust adaptive beamforming algorithms, including ridge regression Capon beamformers (RRCBs), the mid-way (MW) algorithm, the shrinkage based approaches, and iterative beamforming algorithms, namely the iterative adaptive approach (IAA), maximum likelihood based IAA (IAA-ML) and M-SBL (multi-snapshot sparse Bayesian learning). The purpose of these algorithms is to mitigate the negative effects of model errors on the standard Capon beamformer (SCB). We provide a thorough evaluation of these methods under various scenarios and give insights into which algorithm is the best choice under which circumstances.
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target detection and parameter estimation for mimo radar systems
IEEE Transactions on Aerospace and Electronic Systems, 2008Co-Authors: Luzhou Xu, Jian Li, Petre StoicaAbstract:We investigate several target detection and parameter estimation techniques for a multiple-input multiple-output (MIMO) radar system. By transmitting independent waveforms via different antennas, the echoes due to targets at different locations are linearly independent of each other, which allows the direct application of many data-dependent beamforming techniques to achieve high resolution and excellent interference rejection capability. In the absence of array steering vector errors, we discuss the application of several existing data-dependent beamforming algorithms including Capon, APES (amplitude and phase estimation) and CAPES (combined Capon and APES), and then propose an alternative estimation procedure, referred to as the combined Capon and approximate maximum likelihood (CAML) method. Via several numerical examples, we show that the proposed CAML method can provide excellent estimation accuracy of both target locations and target amplitudes. In the presence of array steering vector errors, we apply the robust Capon beamformer (RCB) and doubly constrained robust Capon beamformer (DCRCB) approaches to the MIMO radar system to achieve accurate parameter estimation and superior interference and jamming suppression performance.
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constant beamwidth and constant powerwidth wideband robust Capon beamformers for acoustic imaging
Journal of the Acoustical Society of America, 2004Co-Authors: Zhisong Wang, Petre Stoica, Toshikazu Nishida, Mark SheplakAbstract:Constant-beamwidth and constant-powerwidth wideband robust Capon beamformers for acoustic imaging
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doubly constrained robust Capon beamformer
IEEE Transactions on Signal Processing, 2004Co-Authors: Petre Stoica, Zhisong WangAbstract:The standard Capon beamformer (SCB) is known to have better resolution and much better interference rejection capability than the standard data-independent beamformer when the array steering vector is accurately known. However, the major problem of the SCB is that it lacks robustness in the presence of array steering vector errors. In this paper, we will first provide a complete analysis of a norm constrained Capon beamforming (NCCB) approach, which uses a norm constraint on the weight vector to improve the robustness against array steering vector errors and noise. Our analysis of NCCB is thorough and sheds more light on the choice of the norm constraint than what was commonly known. We also provide a natural extension of the SCB, which has been obtained via covariance matrix fitting, to the case of uncertain steering vectors by enforcing a double constraint on the array steering vector, viz. a constant norm constraint and a spherical uncertainty set constraint, which we refer to as the doubly constrained robust Capon beamformer (DCRCB). NCCB and DCRCB can both be efficiently computed at a comparable cost with that of the SCB. Performance comparisons of NCCB, DCRCB, and several other adaptive beamformers via a number of numerical examples are also presented.
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versatile robust Capon beamforming theory and applications
Sensor Array and Multichannel Signal Processing Workshop, 2004Co-Authors: Petre StoicaAbstract:The standard Capon beamformer (SCB) has better resolution and much better interference rejection capability than the data-independent beamformer provided that the array steering vector corresponding to the signal-of-interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the data-independent beamformer. Most of the early suggested robust adaptive methods are rather ad hoc in that the choice of their parameters are not directly related to the uncertainly of the steering vector. In this paper we provide a review of the recently proposed robust Capon beam-former (RCB) and doubly constrained robust Capon beamformer (DCRCB), which directly address the uncertainty of the steering vector and naturally extend the covariance fitting formulation of SCB to the case of uncertain steering vectors by enforcing a double constraint on the steering vector, viz. a constant norm constraint and an uncertainty set constraint. We also present several extensions and applications of RCB including constant-powerwidth RCB (CPRCB) and constant-beamwidth RCB (CBRCB) for acoustic imaging, rank-deficient robust Capon filter-bank (RCF) approach for spectral estimation, and rank-deficient RCB for landmine detection using forward-looking ground penetrating radar (FLGPR) imaging systems. The excellent performances of RCB, DCRCB, and the various extensions of RCB are demonstrated by simulated and experimental examples.
Zhisong Wang - One of the best experts on this subject based on the ideXlab platform.
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constant beamwidth and constant powerwidth wideband robust Capon beamformers for acoustic imaging
Journal of the Acoustical Society of America, 2004Co-Authors: Zhisong Wang, Petre Stoica, Toshikazu Nishida, Mark SheplakAbstract:Constant-beamwidth and constant-powerwidth wideband robust Capon beamformers for acoustic imaging
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doubly constrained robust Capon beamformer
IEEE Transactions on Signal Processing, 2004Co-Authors: Petre Stoica, Zhisong WangAbstract:The standard Capon beamformer (SCB) is known to have better resolution and much better interference rejection capability than the standard data-independent beamformer when the array steering vector is accurately known. However, the major problem of the SCB is that it lacks robustness in the presence of array steering vector errors. In this paper, we will first provide a complete analysis of a norm constrained Capon beamforming (NCCB) approach, which uses a norm constraint on the weight vector to improve the robustness against array steering vector errors and noise. Our analysis of NCCB is thorough and sheds more light on the choice of the norm constraint than what was commonly known. We also provide a natural extension of the SCB, which has been obtained via covariance matrix fitting, to the case of uncertain steering vectors by enforcing a double constraint on the array steering vector, viz. a constant norm constraint and a spherical uncertainty set constraint, which we refer to as the doubly constrained robust Capon beamformer (DCRCB). NCCB and DCRCB can both be efficiently computed at a comparable cost with that of the SCB. Performance comparisons of NCCB, DCRCB, and several other adaptive beamformers via a number of numerical examples are also presented.
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doubly constrained robust Capon beamformer
Asilomar Conference on Signals Systems and Computers, 2003Co-Authors: Petre Stoica, Zhisong WangAbstract:The standard Capon beamformer (SCB) is known to have better resolution and much better interference rejection capability than the standard data-independent beamformer when the array steering vector is accurately known. However, the major problem of SCB is that it lacks robustness in the presence of array steering vector errors. In this paper, we provide a natural extension of SCB, obtained via covariance matrix fitting, to the case of uncertain steering vectors by enforcing a double constraint on the array steering vector, viz. a constant norm constraint and a spherical uncertainty set constraint, which we refer to as the doubly constrained robust Capon beamformer (DCRCB). DCRCB can be efficiently computed at a comparable cost with that of SCB. Performance comparisons of DCRCB and our previously proposed robust Capon beamformer (RCB) are also presented via a number of numerical examples.
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on robust Capon beamforming and diagonal loading
International Conference on Acoustics Speech and Signal Processing, 2003Co-Authors: Jian Li, Petre Stoica, Zhisong WangAbstract:Whenever the knowledge of the array steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the standard beamformer. Diagonal loading (including its extended versions) has been a popular approach to improve the robustness of the Capon beamformer. In this paper we show that a natural extension of the Capon beamformer to the case of uncertain steering vectors also belongs to the class of diagonal loading approaches but the amount of diagonal loading can be precisely calculated based on the uncertainty set of the steering vector. The proposed robust Capon beamformer can be efficiently computed at a comparable cost with that of the standard Capon beamformer. Its excellent performance is demonstrated via a number of numerical examples.
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Robust Capon beamforming
IEEE Signal Processing Letters, 2003Co-Authors: Petre Stoica, Zhisong Wang, Jian LiAbstract:The Capon beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the standard beamformer. We present a natural extension of the Capon beamformer to the case of uncertain steering vectors. The proposed robust Capon beamformer can no longer be expressed in a closed form, but it can be efficiently computed. Its excellent performance is demonstrated via a number of numerical examples.
Christ D Richmond - One of the best experts on this subject based on the ideXlab platform.
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sample covariance based estimation of Capon algorithm error probabilities
Asilomar Conference on Signals Systems and Computers, 2010Co-Authors: Christ D Richmond, Robert L Geddes, Ramis Movassagh, Alan EdelmanAbstract:The method of interval estimation (MIE) provides a strategy for mean squared error (MSE) prediction of algorithm performance at low signal-to-noise ratios (SNR) below estimation threshold where asymptotic predictions fail. MIE interval error probabilities for the Capon algorithm are known and depend on the true data covariance and assumed signal array response. Herein estimation of these error probabilities is considered to improve representative measurement errors for parameter estimates obtained in low SNR scenarios, as this may improve overall target tracking performance. A statistical analysis of Capon error probability estimation based on the data sample covariance matrix is explored herein.
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statistical analysis of Capon bartlett 2 d cross spectrum
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Christ D RichmondAbstract:An exact joint probability density function (PDF) (not approximate as in [4]) is provided for the Capon power spectral estimate and the average output power of any other deterministic filter when based on the same data sample covariance matrix. The cross coherence/cosine (correlation coefficient) between the two filter weights determines the extent of statistical dependence. An exact PDF for a sample covariance based (SCB) estimate of this cross coherence is derived allowing complete point-level statistical characterization of the 2-D Capon-Bartlett cross spectrum introduced in [4]. The exact bias and variance are computed for the cross spectrum showing asymptotic consistency and inviting a natural recursive refinement for reducing bias.
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cross coherence and joint pdf of the bartlett and Capon power spectral estimates
International Conference on Acoustics Speech and Signal Processing, 2007Co-Authors: Christ D RichmondAbstract:The Bartlett algorithm results from a conventional (Fourier or beamforming) approach to power spectral estimation and the Capon algorithm results from an adaptive approach. Both algorithms make use of the data sample covariance matrix (SCM). The Bartlett algorithm relies directly on the SCM, while the Capon approach relies on the inverse of the SCM. Since both statistics depend on the same data, they are not independent in general. While the marginal distribution of each statistic is well-known, the joint dependence is unknown. This paper presents a complete statistical summary of the joint dependence of the Bartlett and Capon statistics, showing that the dependence is expressible via a 2 times 2 complex Wishart matrix where the coupling is determined by a single measure of coherence defined herein. Interestingly, this measure of coherence leads to a new two-dimensional algorithm capable of yielding better resolution than the Capon algorithm.
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Capon algorithm mean squared error threshold snr prediction and probability of resolution
IEEE Transactions on Signal Processing, 2005Co-Authors: Christ D RichmondAbstract:Below a specific threshold signal-to-noise ratio (SNR), the mean-squared error (MSE) performance of signal parameter estimates derived from the Capon algorithm degrades swiftly. Prediction of this threshold SNR point is of practical significance for robust system design and analysis. The exact pairwise error probabilities for the Capon (and Bartlett) algorithm, derived herein, are given by simple finite sums involving no numerical integration, include finite sample effects, and hold for an arbitrary colored data covariance. Via an adaptation of an interval error based method, these error probabilities, along with the local error MSE predictions of Vaidyanathan and Buckley, facilitate accurate prediction of the Capon threshold region MSE performance for an arbitrary number of well separated sources, circumventing the need for numerous Monte Carlo simulations. A large sample closed-form approximation for the Capon threshold SNR is provided for uniform linear arrays. A new, exact, two-point measure of the probability of resolution for the Capon algorithm, that includes the deleterious effects of signal model mismatch, is a serendipitous byproduct of this analysis that predicts the SNRs required for closely spaced sources to be mutually resolvable by the Capon algorithm. Last, a general strategy is provided for obtaining accurate MSE predictions that account for signal model mismatch.
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Capon and bartlett beamforming threshold effect in direction of arrival estimation error and on the probability of resolution
2005Co-Authors: Christ D RichmondAbstract:Abstract : Below a specific threshold signal-to-noise ratio (SNR), the mean squared error (MSE) perfor- mance of signal direction-of arrival (DOA) estimates derived from the Capon algorithm degrades swiftly. Prediction of this threshold SNR point is of practical significance for robust system design and analysis. The exact pairwise error probabilities for the Capon (and Bartlett) algorithm are derived herein, given by simple finite sums involving no numerical integration, include finite sample effects, and hold for an arbitrary colored data covariance. An accurate large sample approximation of these error probabilities in terms of the well tabulated complementary error function is also provided. Via an adaptation of an interval error-based method, these error probabilities, along with the local error MSE predictions of Vaidyanathan and Buckley, facilitate accurate prediction of the Capon threshold region DOA MSE performance for an arbitrary number of well separated sources, circumventing the need for numerous Monte Carlo simulations. A large sample closed form approximation for the Capon (and Bartlett) threshold SNR is provided for uniform linear arrays. A new exact two-point measure of the Capon probability of resolution, that includes the deleterious effects of signal model mismatch, is a serendipitous by-product of this analysis that predicts the SNRs required for closely spaced sources to be mutually resolvable by the Capon algorithm. Lastly, a new general strategy is provided for obtaining accurate MSE predictions that account for signal model mismatch.
Jian Li - One of the best experts on this subject based on the ideXlab platform.
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target detection and parameter estimation for mimo radar systems
IEEE Transactions on Aerospace and Electronic Systems, 2008Co-Authors: Luzhou Xu, Jian Li, Petre StoicaAbstract:We investigate several target detection and parameter estimation techniques for a multiple-input multiple-output (MIMO) radar system. By transmitting independent waveforms via different antennas, the echoes due to targets at different locations are linearly independent of each other, which allows the direct application of many data-dependent beamforming techniques to achieve high resolution and excellent interference rejection capability. In the absence of array steering vector errors, we discuss the application of several existing data-dependent beamforming algorithms including Capon, APES (amplitude and phase estimation) and CAPES (combined Capon and APES), and then propose an alternative estimation procedure, referred to as the combined Capon and approximate maximum likelihood (CAML) method. Via several numerical examples, we show that the proposed CAML method can provide excellent estimation accuracy of both target locations and target amplitudes. In the presence of array steering vector errors, we apply the robust Capon beamformer (RCB) and doubly constrained robust Capon beamformer (DCRCB) approaches to the MIMO radar system to achieve accurate parameter estimation and superior interference and jamming suppression performance.
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on robust Capon beamforming and diagonal loading
International Conference on Acoustics Speech and Signal Processing, 2003Co-Authors: Jian Li, Petre Stoica, Zhisong WangAbstract:Whenever the knowledge of the array steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the standard beamformer. Diagonal loading (including its extended versions) has been a popular approach to improve the robustness of the Capon beamformer. In this paper we show that a natural extension of the Capon beamformer to the case of uncertain steering vectors also belongs to the class of diagonal loading approaches but the amount of diagonal loading can be precisely calculated based on the uncertainty set of the steering vector. The proposed robust Capon beamformer can be efficiently computed at a comparable cost with that of the standard Capon beamformer. Its excellent performance is demonstrated via a number of numerical examples.
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Robust Capon beamforming
IEEE Signal Processing Letters, 2003Co-Authors: Petre Stoica, Zhisong Wang, Jian LiAbstract:The Capon beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the standard beamformer. We present a natural extension of the Capon beamformer to the case of uncertain steering vectors. The proposed robust Capon beamformer can no longer be expressed in a closed form, but it can be efficiently computed. Its excellent performance is demonstrated via a number of numerical examples.
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On robust Capon beamforming and diagonal loading
IEEE Transactions on Signal Processing, 2003Co-Authors: Jian Li, Petre Stoica, Zhisong WangAbstract:The Capon (1969) beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the standard beamformer. Diagonal loading (including its extended versions) has been a popular approach to improve the robustness of the Capon beamformer. We show that a natural extension of the Capon beamformer to the case of uncertain steering vectors also belongs to the class of diagonal loading approaches, but the amount of diagonal loading can be precisely calculated based on the uncertainty set of the steering vector. The proposed robust Capon beamformer can be efficiently computed at a comparable cost with that of the standard Capon beamformer. Its excellent performance for SOI power estimation is demonstrated via a number of numerical examples.
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robust Capon beamforming
Asilomar Conference on Signals Systems and Computers, 2002Co-Authors: Petre Stoica, Zhisong Wang, Jian LiAbstract:The Capon beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of interest (SOI) is accurately known. However, whenever the knowledge of SOI steering vector is imprecise, the performance of the Capon beamformer may become worse than that of the standard beamformer. In this paper, we present a natural extension of the Capon beamformer to the case of uncertain steering vectors. The proposed robust Capon beamformer can no longer be expressed in a closed form but it can be efficiently computed. Its excellent performance is demonstrated via a number of numerical examples.
Solomon H. Snyder - One of the best experts on this subject based on the ideXlab platform.
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Neuronal nitric-oxide synthase localization mediated by a ternary complex with synapsin and Capon.
Proceedings of the National Academy of Sciences of the United States of America, 2002Co-Authors: Samie R. Jaffrey, Andrew J Czernik, Adele M. Snowman, Solomon H. SnyderAbstract:The specificity of the reactions of nitric oxide (NO) with its neuronal targets is determined in part by the precise localizations of neuronal NO synthase (nNOS) within the cell. The targeting of nNOS is mediated by adapter proteins that interact with its PDZ domain. Here, we show that the nNOS adapter protein, Capon, interacts with synapsins I, II, and III through an N-terminal phosphotyrosine-binding domain interaction, which leads to a ternary complex comprising nNOS, Capon, and synapsin I. The significance of this ternary complex is demonstrated by changes in subcellular localization of nNOS in mice harboring genomic deletions of both synapsin I and synapsin II. These results suggest a mechanism for specific actions of NO at presynaptic sites.
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Capon a protein associated with neuronal nitric oxide synthase that regulates its interactions with psd95
Neuron, 1998Co-Authors: Samie R. Jaffrey, Adele M. Snowman, Mikael J L Eliasson, Noam A Cohen, Solomon H. SnyderAbstract:Nitric oxide (NO) produced by neuronal nitric oxide synthase (nNOS) is important for N-methyl-D-aspartate (NMDA) receptor-dependent neurotransmitter release, neurotoxicity, and cyclic GMP elevations. The coupling of NMDA receptor-mediated calcium influx and nNOS activation is postulated to be due to a physical coupling of the receptor and the enzyme by an intermediary adaptor protein, PSD95, through a unique PDZ-PDZ domain interaction between PSD95 and nNOS. Here, we report the identification of a novel nNOS-associated protein, Capon, which is highly enriched in brain and has numerous colocalizations with nNOS. Capon interacts with the nNOS PDZ domain through its C terminus. Capon competes with PSD95 for interaction with nNOS, and overexpression of Capon results in a loss of PSD95/nNOS complexes in transfected cells. Capon may influence nNOS by regulating its ability to associate with PSD95/NMDA receptor complexes.
