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Zhang Y.,Anhui University of Science and Technology | Zhang Y.,National Engineering Laboratory for Speech and Language Information Processing | Hu N.,Anhui University of Science and Technology | Hu N.,National Engineering Laboratory for Speech and Language Information Processing | And 2 more authors.
Signal Processing | Year: 2013

A new method is proposed for sources number detection in array signal processing. This method is based on the orthogonality of signal subspace and noise subspace. At first obtain a set of bootstrap snapshots from original snapshots. Then estimate the direction of an arbitrary incident source, make eigen-decomposition of the covariance matrix and compute the weighted inner product vector. Subsequently repeat the above procedures many times to get weighted inner product vectors and calculate the average of them. Finally employ a clustering algorithm to determine the number of sources. The simulation results show the superiority of the proposed method at small number of snapshots and/or low signal-to-noise ratio (SNR). © 2012 Elsevier B.V. All rights reserved. Source


Hu N.,Anhui University of Science and Technology | Hu N.,National Engineering Laboratory for Speech and Language Information Processing | Xu X.,Anhui University of Science and Technology | Xu X.,National Engineering Laboratory for Speech and Language Information Processing | And 2 more authors.
Signal Processing | Year: 2014

The idea of block-sparse signal reconstruction, as an alternative perspective compared with the conventional approach, is exploited to formulate the problem of direction-of-arrival (DOA) estimation for wideband signals. Prolate spheroidal wave functions (PSWFs) are used to form the block-wise bases for this problem, due to its excellent performance in extrapolating bandlimited signals, and the block orthogonal matching pursuit (BOMP) algorithm is directly employed to verify its efficiency. Simulation results show that the proposed method yields better performance when the number of samples is highly limited. © 2013 Elsevier B.V. Source


Xu X.,University of Electronic Science and Technology of China | Xu X.,National Engineering Laboratory for Speech and Language Information Processing | Ye Z.,University of Electronic Science and Technology of China | Ye Z.,National Engineering Laboratory for Speech and Language Information Processing
IET Radar, Sonar and Navigation | Year: 2012

In this study, a new two-dimensional direction of arrival (2D DOA) estimation method is proposed for a uniform rectangular array (URA). The impinging signals are a mixture of uncorrelated and coherent signals. The method consists of two steps. The DOAs of uncorrelated signals are first estimated by a modified 2D estimation of signal parameters via rotational invariance techniques (ESPRIT). Then the contributions of uncorrelated signals and noises are eliminated after performing a subtraction operation on the elements of the covariance matrix and only those of coherent signals remain. Based on these subtracted elements, a decorrelating matrix with a larger size is constructed to estimate the DOAs of coherent signals. These two-step processes can be carried out in parallel because there is no inherent relationship between them. The proposed method has high estimation precision, needs no 2D angle searching and is suitable for the array no matter whether the number of sensors is odd or even. Simulation results demonstrate the effectiveness and performance of the proposed method. © 2012 The Institution of Engineering and Technology. Source


Hu N.,Anhui University of Science and Technology | Hu N.,National Engineering Laboratory for Speech and Language Information Processing | Ye Z.,Anhui University of Science and Technology | Ye Z.,National Engineering Laboratory for Speech and Language Information Processing | And 4 more authors.
Signal Processing | Year: 2012

A new algorithm involving sparse recovery is proposed to address the problem of direction-of-arrival (DOA) estimation using weighted subspace fitting (WSF). The proposed algorithm proves to be a modified version of ℓ 1-SVD by using an optimal weighting matrix, wherein a scheme of regularization between sparsity penalty and subspace fitting error is also given for all SNR range. Numerical simulations verify the efficiency of the proposed algorithm and illustrate the performance improvement in low SNR. © 2012 Elsevier B.V. All rights reserved. Source


Zhang Y.,Anhui University of Science and Technology | Zhang Y.,National Engineering Laboratory for Speech and Language Information Processing | Ye Z.,Anhui University of Science and Technology | Ye Z.,National Engineering Laboratory for Speech and Language Information Processing | And 4 more authors.
Signal Processing | Year: 2014

A new method based on a novel model for off-grid direction-of-arrival (DOA) estimation is presented. The novel model is based on the sample covariance matrix and the off-grid representation of the steering vector. Based on this model, its equivalent signals are assumed to satisfy independent Gaussian distribution and its noise variance can be normalized to 1. The off-grid DOAs are estimated by the block sparse Bayesian algorithm. The advantages of the proposed method are that it considers the temporal correlation existed in each row of the equivalent signal sample matrix and the normalized noise variance does not need to be estimated. Moreover, this algorithm can work without the knowledge of the number of signals. Numerical simulations demonstrate the superior performance of the proposed method. © 2013 Elsevier B.V. Source

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