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8th China Wireless sensor Network Conference (CWSN)

作者： Nie, Weike Feng, Dazheng Fang, Dingyi Chen, Xiaojiang NW Univ Xian Sch Informat Sci & Technol Xian 710069 Peoples R China Xidian Univ Natl Key Lab Radar Signal Proc Xian Peoples R China

ISBN: (纸本)9783662469811;9783662469804

Based on data matrix, a novel algorithm termed multiple bi-direction optimization (MBDO) is proposed to implement two-dimension (2-D) adaptive beamforming and interference sources localization. The main contribution of MBDO is to process the long weight vector. We decomposed the long weight vector into two short weight vectors. MBDO is used to compute the two short weight vectors by data matrix to finish beamforming. Moreover, the inverse of high dimension correlation matrix is replaced by low dimension data matrix. Theoretical analysis and simulations are provided to demonstrate the significant reduction in computational complexity and the improvement in performance compared with the well-known LCMV beamforming in most cases.

关键词： sensor array signal processing Data domain Interference location MBDO algorithm

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A New Methodology for Detecting Source Number

A New Methodology for Detecting Source Number

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Conference on Medical Imaging 2011 - Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Lei, Tianhu Roberts, Timothy P. L. Childrens Hosp Philadelphia Dept Radiol Philadelphia PA 19104 USA Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9780819485076

Current methods for detecting source number in magnetic source image (MSI) have some shortcomings: i) they are somewhat subjective, ii) various MSIs employ different indexes, and iii) the number of the potential sources in MSI via Beamforming may be underestimated. The new approach extracts a principal component from each trial which serves as a (one) representative virtual channel for that trial. Principal components from all trials are combined to form a (one) synthesized trial. The information criterion is applied to this synthesized trial to select an optimal estimate of the source number. PCA reduces noise and captures the greatest variability in each trial, the synthesized trial reduces data fluctuations among trials and also reduces the dimensions of the covariance matrix. The approach is entirely data-driven and is suitable for detecting seizures and interictal spikes in epilepsy patients.

关键词： MEG neuroimaging sensor array signal processing Information theoretic criteria Multivariate analysis Principal component

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Compressive MUSIC with Optimized Partial Support for Joint Sparse Recovery

Compressive MUSIC with Optimized Partial Support for Joint S...

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IEEE International Symposium on Information Theory (ISIT)

作者： Kim, Jong Min Lee, Ok Kyun Ye, Jong Chul Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Taejon 305701 South Korea

ISBN: (纸本)9781457705953

The multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. The MMV problem has been traditionally addressed either by sensor array signal processing or compressive sensing. However, recent breakthroughs in this area such as compressive MUSIC (CS-MUSIC) or subspace-augumented MUSIC (SA-MUSIC) optimally combine the compressive sensing (CS) and array signal processing such that k-r supports are first found by CS and the remaining r supports are determined by a generalized MUSIC criterion, where k and r denote the sparsity and the number of independent snapshots, respectively. Even though such a hybrid approach significantly outperforms the conventional algorithms, its performance heavily depends on the correct identification of k - r partial support by the compressive sensing step, which often deteriorates the overall performance. The main contribution of this paper is, therefore, to show that as long as k - r + 1 correct supports are included in any k sparse CS solution, the optimal k - r partial support can be found using a subspace fitting criterion, significantly improving the overall performance of CS-MUSIC. Furthermore, unlike the single measurement CS counterpart that requires infinite SNR for a perfect support recovery, we can derive an information theoretic sufficient condition for the perfect recovery using CS-MUSIC under a finite SNR scenario.

关键词： Compressed sensing Joints MMV problem Multiple signal classification Particle measurements sensors signal to noise ratio array signal processing compressive MUSIC sensing data compression finite SNR scenario generalized MUSIC criterion information theory joint sparse recovery k-r partial support identification multiple measurement vector problem sensor array signal processing signal classification signal reconstruction subspace fitting criterion subspace-augumented MUSIC

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Source Enumeration for High-Resolution array processing Using Improved Gerschgorin Radii Without Eigendecomposition

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IEEE TRANSACTIONS ON signal processing 2008年第12期56卷 5916-5925页

作者： Huang, Lei Long, Teng Wu, Shunjun Beijing Inst Technol Dept Elect Engn Beijing 100081 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Accurate detection of sources with low complexity is of considerable interest in practical applications of high-resolution array processing. This paper addresses a new computationally efficient method for source enumeration by using enhanced Gerschgorin radii without eigendecomposition. The proposed method can calculate the Gerschgorin radii in a more efficient manner, in which the additive background noise can be efficiently suppressed and the computational complexity can be considerably reduced. Therefore, the method is more accurate and computationally attractive. Furthermore, the method does not rely on the eigenvalues of a covariance matrix or the signal/noise power, making it robust against deviations from the assumption of spatially white noise model. Numerical results are presented to demonstrate the performance of the method.

关键词： Direction finding eigenvalue decomposition (EVD) Gerschgorin radii high resolution minimum description length (MDL) multistage Wiener filter (MSWF) sensor array signal processing signal enumeration

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Reduced-rank MDL method for source enumeration in high-resolution array processing

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IEEE TRANSACTIONS ON signal processing 2007年第12期55卷 5658-5667页

作者： Huang, Lei Wu, Shunjun Li, Xia Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

This paper proposes a reduced-rank minimum description length (MDL) method to enumerate the incident waves impinging on a uniform linear array (ULA). First, a new observation data and a reference signal are formed from sensor data by means of the shift-invariance property of the ULA. A cross-correlation between them is calculated, which is able to capture signal information and efficiently suppress additive noise. Second, the normalized cross-correlation is used as initial information for a recursion procedure to quickly partition the observation data into two orthogonal components in a signal subspace and a reduced-rank noise subspace. The components in the noise subspace are employed to calculate the total code length that is required to encode the observation data. Finally, the model with the shortest code length, namely the minimum description length, is chosen as the best model. Unlike the traditional MDL methods, this method partitions the observation data into the cleaner signal and noise subspace components by means of the recursion procedure, avoiding the estimation of a covariance matrix and its eigendecomposition. Thus, the method has the advantage of computational simplicity. Its performance is demonstrated via numerical results.

关键词： direction-of-arrival (DOA) eigenvalue decomposition (EVD) high resolution minimum description length (MDL) multistage Wiener filter (MSWF) reduced rank sensor array signal processing signal enumeration Wiener filter

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Recursion subspace-based method for bearing estimation

Recursion subspace-based method for bearing estimation

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3rd International Workshop on signal Design and Its Applications in Communications (IWSDA 07)

作者： Huang, Lei Li, Xia Cai, Liangwei Wu, Shunjun Shenzhen Univ Coll Informat Engn Shenzhen 518060 Guangdong Peoples R China Xidian Univ Nat Lab Radar Signal Processing Xian 710071 Peoples R China

ISBN: (纸本)9781424410736

In this paper, a recursion subspace-based method (RSM) is presented to estimate the bearings of incident signals impinging on a uniform linear array (ULA). Unlike the existing linear operation based methods, such as the signal subspace method without eigendecomposition (SUMWE) of Xin et al, that calculate the noise subspace, the RSM method finds a cleaner signal subspace by means of a successive recursion procedure. Thus, the RSM method has the advantages of computational simplicity and accurate estimation, in particular for a large array. Numerical results are presented to compare the performance the RSM method with that of the SUMWE methods.

关键词： sensor array signal processing bearing estimation forward/backward spatial smoothing (FBSS)

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Recursion Subspace-Based Method for Bearing Estimation

Recursion Subspace-Based Method for Bearing Estimation

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第三届信号设计及其在通信中的应用国际会议(Proceedings of 2007 International Workshop on signal Design and Its Applications in Communications IWSDA'07)

作者： Lei HUANG Xia LI Liangwei CAI Shunjun WU College of Information Engineering Shenzhen University Shenzhen Guangdong 518060 China National Lab of Radar Signal Processing Xidian University Xi'an 710071 China

In this paper, a recursion subspace-based method (RSM) is presented to estimate the bearings of incident signals impinging on a uniform linear array (ULA).Unlike the existing linear operation based methods, such as the signal subspace method without eigendecomposition (SUMWE) of Xin et al, that calculate the noise subspace,the RSM method finds a cleaner signal subspace by means of a successive recursion procedure. Thus, the RSM method has the advantages of computational simplicity and accurateestimation, in particular for a large array. Numerical results are presented to compare the performance the RSM method with that of the SUMWE methods.

关键词： sensor array signal processing Bearing estimation. Forward/backward spatial smoothing ( FBSS).

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Description of statistical theory of Magnetic Resonance Imaging (MRI)

Description of statistical theory of Magnetic Resonance Imag...

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Medical Imaging 2006 Conference

作者： Lei, Tianhu Univ Penn Dept Radiol Philadelphia PA 19104 USA

ISBN: (纸本)0819461857

The underlying phenomena in Magnetic Resonance imaging (MRI) physics, reconstruction, and analysis can be described by their statistics. This paper reports some new developments and focuses on two aspects: (1) new insights into statistical theory of MRI beyond its conclusions, and (2) three new applications of this theory spanned from image analysis to imaging physics. Why MR signals as well as k-space samples are statistically independent? When k-space samples are independent, why pixels in the reconstructed images are correlated? How does this correlation arise? Are spatially asymptotic independence and exponential correlation coefficient consistent? Why the homogeneity and scales can be characterized by stationarity and ergodicity? This paper provides answers to these questions. The first application of statistical theory of MRI is a stochastic model-based image analysis approach. The second application is a sensor array processing approach for detecting distinctive object regions. The third application is the Parallel Magnetic Resonance imaging (P-MRI) which shows by combining statistics of MRI and sensor array processing, P-MRI can be easily formulated. This paper shows that statistical theory of MRI not only provides basis for MR imaging, reconstruction, and analysis, but also offers means to integrate signal processing methods and image processing methods for solving various MRI related problems.

关键词： Magnetic Resonance Imaging statistics stochastic image model finite normal mixture sensor array signal processing parallel MRI

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Beamforming using the fractional Fourier transform

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IEEE TRANSACTIONS ON signal processing 2003年第6期51卷 1663-1668页

作者： Yetik, IS Nehorai, A Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

We present a new method of beamforming using the fractional courier transform (FrFT). This method encompasses the conventional minimum mean-squared error (MSE) beamforming in the frequency domain or spatial domain as special cases. It is especially useful for applications involving chiro,signals such as signal enhancement problems with accelerating sinusoidal sources where the Doppler effect generates chirp signals and a frequency shift and active radar problems where chirp signals are transmitted. Numerical examples demonstrate the potential advantage of the proposed method over the ordinary frequency or spatial domain beamforming for a moving source scenario.

关键词： Beamforming Fractional Fourier transform Time-frequency analysis sensor array signal processing

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A class of subspace tracking algorithms based on approximation of the noise-subspace

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IEEE TRANSACTIONS ON signal processing 2000年第11期48卷 3231-3235页

作者： Gustafsson, T MacInnes, CS Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

This correspondence introduces a novel class of so-called subspace tracking algorithms applicable to, for example, sensor array signal processing. The basic idea pursued in this correspondence is to reduce the amount of computations required for an exact SVD update, applying a perturbation-like strategy, which is interpreted as an approximation of a noise subspace, An interesting property of the derived algorithms is that they can be applied to SVD updating of both auto- and cross-covariance matrices.

关键词： sensor array signal processing subspace tracking

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