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A source tracking sensor array testbed

MICROPROCESSORS AND MICROSYSTEMS 1999年第4期23卷 207-216页

作者： Scott, ED Pierre, JW Hamann, JC Univ Wyoming Dept Elect Engn Laramie WY 82071 USA

The University of Wyoming Source Tracking array Testbed (UW STAT) was developed as a platform to experimentally investigate recent advances in sensor array source tracking algorithms. UW STAT is a six sensor array with one or more sources which may be moving. This system is unique in its compactness and its emphasis on the source tracking problem with the ability to precisely know the true source location for comparison with array tracking algorithm results. Experimental tracking results are presented to demonstrate the performance of the system. Experimental data, along with information about UW STAT, is available on the World Wide Web (http://***/electrical/***) for other researchers to test their tracking algorithms. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： sensor array processing source tracking signal processing

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New Approaches to Direction-of-Arrival Estimation With sensor arrays in Unknown Nonuniform Noise

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IEEE sensorS JOURNAL 2016年第24期16卷 8982-8989页

作者： Liao, Bin Huang, Lei Guo, Chongtao So, Hing Cheung Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

It is known that classical subspace-based direction-of-arrival (DOA) estimation algorithms are not straightforwardly applicable to scenarios with unknown spatially nonuniform noise. Among the state-of-the-art solutions, this problem is tackled by iterative subspace estimation algorithms to incorporate subspace-based approaches or nonlinear optimization routines to bypass the direct identification of subspaces. In this paper, the problem of DOA estimation in nonuniform noise is revisited by devising two computationally efficient proposals. It is proved herein that, if the signals are uncorrelated, the signal and noise subspaces can be directly obtained from the eigendecomposition of a reduced array covariance matrix. On the other hand, when the signals are correlated, the estimation of the noise covariance matrix is formulated into a rank minimization problem which can be approximately solved by semidefinite programming. In both cases, the signal and noise subspaces are easy to compute without iterations. Consequently, classical subspace-based algorithms can be employed to determine the DOAs. Numerical examples are provided to demonstrate the performance and applicability of the proposed methods.

关键词： sensor array processing direction-of-arrival estimation nonuniform noise semidefinite programming

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Blind Gain and Phase Calibration via Sparse Spectral Methods

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第5期65卷 3097-3123页

作者： Li, Yanjun Lee, Kiryung Bresler, Yoram Univ Illinois Urbana IL 61801 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly. BGPC arises in numerous applications, e.g., blind albedo estimation in inverse rendering, synthetic aperture radar autofocus, and sensor array auto-calibration. In some cases, sparse structure in the unknown signal alleviates the illposedness of BGPC. Recently, there has been renewed interest in solutions to BGPC with careful analysis of error bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem and propose to solve it via power iteration, or in the sparsity or joint sparsity case, via truncated power iteration. Under certain assumptions, the unknown gains, phases, and the unknown signal can be recovered simultaneously. Numerical experiments show that power iteration algorithms work not only in the regime predicted by our main results, but also in regimes where theoretical analysis is limited. We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e.g., with noisy measurement or with a bad initial estimate.

关键词： Auto-calibration greedy algorithm inverse rendering multichannel blind deconvolution nonconvex optimization power method SAR autofocus sensor array processing truncated power iteration

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Performance analysis of the approximate dynamic programming algorithm for parameter estimation of superimposed signals

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IEEE TRANSACTIONS ON SIGNAL processing 2000年第5期48卷 1274-1286页

作者： Yau, SF Bresler, Y Integrated Display Technol Ltd Hong Kong Hong Kong Peoples R China Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA

We consider the classical problem of fitting a model composed of multiple superimposed signals to noisy data using the criteria of maximum likelihood (ML) or subspace fitting, jointly termed generalized subspace fitting (GSF), We analyze a recently proposed: approximate dynamic programming algorithm (ADP), which provides a computationally efficient solution to the associated multidimensional multimodal optimization problem. We quantify the error introduced by the approximations in ADP and deviations from the key local interaction signal model (LISMO) modeling assumption in two ways. First, we upper bound the difference between the exact minimum of the GSF criterion and its value at the ADP estimate and compare the ADP with GSF estimates obtained by exhaustive multidimensional search on a fine,lattice. Second, motivated by the similar accuracy bounds, we use perturbation analysis to derive approximate expressions for the MSE of the ADP estimates. These various results provide, for the first time, an effective tool to predict the performance of the ADP algorithm for various signal models at nonasymptotic conditions of interest in practical applications. In particular, they :demonstrate that for the classical problems of sinusoid retrieval and array processing, ADP performs comparably to enact (but expensive) maximum likelihood (ML) over a wide range df signal-to-noise ratios (SNR's) and is therefore an attractive algorithm.

关键词： dynamic programming model fitting nonlinear regression optimization parameter estimation radar sensor array processing sinusoid retrieval sonar

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ON THE SENSITIVITY OF THE ESPRIT ALGORITHM TO NONIDENTICAL SUBarrayS

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 1990年第3期15卷 197-212页

作者： SWINDLEHURST, A KAILATH, T Department of Electrical and Computer Engineering Brigham Young University Provo USA Information Systems Laboratory Stanford University Stanford USA

ESPRIT (estimation of signal parameters via rotational invariance techniques) is a recently introduced algorithm for narrowband direction-of-arrival (DOA) estimation. Its principal advantage is that the DOA parameter estimates are obtained directly, without knowledge (and hence storage) of the array manifold and without computation or search of some spectral measure. This advantage is achieved by constraining the sensor array to be composed of two identical, translationally invariant subarrays. In this paper, we analyse the sensitivity of ESPRIT to the assumption that the subarrays are identical. The analysis is applicable to a wide variety of array errors, including non-identical angle-dependent and angle-independent gain and phase perturbations, errors in the locations of the subarray elements, and mutual coupling effects. A representative simulation example will be presented to validate the analysis and compare the performance degradation of ESPRIT with that of the MUSIC algorithm.

关键词： sensor array processing DIRECTION-OF-ARRIVAL ESTIMATION PERTURBATION ANALYSES ANTENNA ERRORS

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MMSE-Based MDL Method for Accurate Source Number Estimation

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IEEE SIGNAL processing LETTERS 2009年第9期16卷 798-801页

作者： Huang, Lei Long, Teng Mao, Erke So, H. C. Beijing Inst Technol Dept Elect Engn Beijing 100081 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

In civilian communication systems, the signature sequence of the desired signal in training phase is known to the receiver. In this letter, using the mutual information, we bridge the probability density function and minimum mean-square error (MMSE) between the observed data and training sequence of the desired signal, and then employ the MMSE to construct a minimum description length (MDL) criterion for accurate source enumeration. Numerical results demonstrate that the proposed method is superior to existing MDL methods in terms of detection performance particularly for small number of snapshots and/or source angular separation.

关键词： Eigenvalue decomposition minimum description length sensor array processing source number estimation

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Performance analysis of reduced-rank beamformers for estimating dipole source signals using EEG/MEG

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2006年第5期53卷 840-844页

作者： Gutiérrez, D Nehorai, A Dogandzic, A Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Univ Nacl Autonoma Mexico Dept Comp Syst Engn & Automat Inst Res Appl Math & Syst Mexico City 01000 DF Mexico

We study the performance of various beamformers for estimating a current dipole source at a known location using electroencephalography (EEG) and magnetoencephalography (MEG). We present our beamformers in the form of the generalized sidelobe canceler (GSC). Under this structure, the beamformer can be solved by finding a filter that achieves the minimum mean-squared error (MMSE) between the mainbearn response and filtered observed signal. We express the MMSE as a function of the filter's rank and use it as a criterion to evaluate the performance of the beamformers. We do not make any assumptions on the rank of the interference-plus-noise covariance matrix. Instead, we treat it as low-rank and derive a general expression for the MMSE. We present numerical examples to compare the MSE performance of beamformers commonly studied in the literature: principal components (PCs), cross-spectral metrics (CSMs), and eigencanceler (EIG) beamformers. Our results show that good estimates of the dipole source signals can be achieved using reduced-rank beamformers even for low signal-to-noise ratio (SNR) values.

关键词： beamforming dipole source signal electroencephalography low-rank covariance matrix magnetoencephalography sensor array processing

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Effect of Approximate Planar Wavefront on Far-Field Direction Finding

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IEEE COMMUNICATIONS LETTERS 2022年第3期26卷 657-661页

作者： He, Jin Shu, Ting Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Intelligent Sensing & Recognit Shanghai 200240 Peoples R China

Existing sensor array direction-finding algorithms in the open literature simplify the far-field source wavefront as exactly planar in order to facilitate the algorithmic development. In fact, since the wavefront of a point emitter is necessarily spherical, this planar wavefront assumption should actually be approximate, especially for large-scale arrays. Mismatch between the actual and approximate propagation models would introduce a non-random bias on the performance of the direction-finding algorithms. This non-random bias is inevitable due to the mismatch between the algorithm's presumptions and the data it actually processes. This work derives the mathematical expression of this non-random bias, along with several qualitative analyses. The analysis is carried out using the Taylor-series expansion. Also, the effect of the model mismatch on direction-finding algorithms is evaluated numerically.

关键词： Data models Mathematical models Multiple signal classification Approximation algorithms Numerical models sensor arrays Estimation Direction-finding sensor array processing model mismatch spherical wavefront planar wavefront

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Weighted closed-form estimators for blind source separation

Weighted closed-form estimators for blind source separation

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11th IEEE Workshop on Statistical Signal processing (SSP 2001)

作者： Zarzoso, V Herrmann, F Nandi, AK Univ Liverpool Dept Elect Engn & Elect Signal Proc & Commun Grp Liverpool L69 3GJ Merseyside England

ISBN: (纸本)0780370112

This paper investigates a novel closed-form estimation class, so-called weighted estimator (WE). for blind source separation in the basic two-signal problem. Proper combination of previously proposed estimators yields consistent estimates of the separation parameters under general conditions. In the real-mixture case, we determine analytic expressions for the WE asymptotic (large-sample) variance and the source-dependent weight value of the most efficient estimator in the class. By means of the bicomplex-number formalism. the WE is extended to the complex-mixture scenario, for which Cramer-Rao bounds are also derived. Simulations compare the WE with other methods, demonstrating its potential.

关键词： blind source separation estimation theory higher-order statistics. non-Gaussian signal processing sensor array processing

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Gain decomposition methods for radio telescope arrays

Gain decomposition methods for radio telescope arrays

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11th IEEE Workshop on Statistical Signal processing (SSP 2001)

作者： Boonstra, AJ van der Veen, AJ Netherlands Fdn Res Astron ASTRON NL-7991 PD Dwingeloo Netherlands

ISBN: (纸本)0780370112

In radio telescope arrays, the complex receiver gains and sensor noise powers are initially unknown and have to be calibrated. Gain calibration enhances the quality of astronomical sky images and moreover, improve the effectiveness of certain radio telescope phased-array data processing techniques, such as radio interference (RFI) mitigation and beamforming. In this paper we present several closed form and iterative complex gain estimation methods. These methods are analyzed and compared to the Cramer-Rao lower bound for the variance of the estimated gain. The models are tested both on simulated data and on observed telescope data.

关键词： applications in radio astronomy sensor array processing

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