809 research outputs found

    EM Algorithm for Multiple Wideband Source Localization

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    A computationally efficient algorithm using the expectation-maximization (EM) algorithm for multiple wideband source localization in the near field of a sensor array/area is addressed in this thesis. Our idea is to decompose the observed sensor data, which is a superimposition of multiple sources, into the individual components in the frequency domain and then estimate the corresponding location parameters associated with each component separately. Instead of the conventional alternating projection (AP) method, we propose to adopt the EM algorithm in this work; our new method involves two steps, namely Expectation (E-step) and Maximization (M-step). In the E-step, the individual incident source waveforms are estimated. Then, in the M-step, the maximum likelihood estimates of the source location parameters are obtained. These two steps are executed iteratively and alternatively until the pre-defined convergence is reached. The computational complexity comparison between our proposed EM algorithm and the existing AP scheme is investigated. It is shown through Monte Carlo simulations that the computational complexity of the proposed EM algorithm is significantly lower than that of the existing AP algorithm

    Wideband DOA Estimation via Sparse Bayesian Learning over a Khatri-Rao Dictionary

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    This paper deals with the wideband direction-of-arrival (DOA) estimation by exploiting the multiple measurement vectors (MMV) based sparse Bayesian learning (SBL) framework. First, the array covariance matrices at different frequency bins are focused to the reference frequency by the conventional focusing technique and then transformed into the vector form. Then a matrix called the Khatri-Rao dictionary is constructed by using the Khatri-Rao product and the multiple focused array covariance vectors are set as the new observations. DOA estimation is to find the sparsest representations of the new observations over the Khatri-Rao dictionary via SBL. The performance of the proposed method is compared with other well-known focusing based wideband algorithms and the Cramer-Rao lower bound (CRLB). The results show that it achieves higher resolution and accuracy and can reach the CRLB under relative demanding conditions. Moreover, the method imposes no restriction on the pattern of signal power spectral density and due to the increased number of rows of the dictionary, it can resolve more sources than sensors

    Wide-band maximum likelihood direction finding by using the tree-structured EM algorithm

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    Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent Univ., 1996.Thesis (Master's) -- Bilkent University, 1996.Includes bibliographical references leaves 38-43A thorough derivation of the Expectation Maximization (EM) algorithm, which is an iterative numerical method of Maximum Likelihood (ML) estimation, is presented for the case of estimating direction of arrivals of unknown deterministic wide-band signals incident from different directions onto a passive array. For the rec^uired signal estimation, alternative regularized least squares estimation techniques are proposed with significant improvement over the standard least squares techniques. Also, for the angle of arrival estimation of a large number of signals, a tree structured EM algorithm is proposed and compared with the conventional EM approach. Extensive simulation results are presented for comparison of the proposed algorithms with the current high-resolution methods of wide-band direction finding. In order to handle efficiently the case of available parametric prior models on the received waveforms, the required modifications are also given.Çadallı, NailM.S

    High-resolution multipath channel parameter estimation using wavelet analysis

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    This thesis explores the novel use of wavelet analysis as a high-resolution digital signal processing algorithm for multipath channel parameter estimation. The results obtained from this research indicate that this wavelet-based digital signal processing algorithm overcomes the resolution limitation in conventional high-resolution algorithm. This may provide a more cost-effective means of implementing channel sounding equipments for very high-resolution measurements

    Efficient and Robust Signal Detection Algorithms for the Communication Applications

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    Signal detection and estimation has been prevalent in signal processing and communications for many years. The relevant studies deal with the processing of information-bearing signals for the purpose of information extraction. Nevertheless, new robust and efficient signal detection and estimation techniques are still in demand since there emerge more and more practical applications which rely on them. In this dissertation work, we proposed several novel signal detection schemes for wireless communications applications, such as source localization algorithm, spectrum sensing method, and normality test. The associated theories and practice in robustness, computational complexity, and overall system performance evaluation are also provided

    DOA estimation and mutual coupling calibration with the SAGE algorithm

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    AbstractIn this paper, a novel algorithm is presented for direction of arrival (DOA) estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization (SAGE) algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time, our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration

    Multipath channel identification by using global optimization in ambiguity function domain

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    Cataloged from PDF version of article.A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels. (C) 2011 Elsevier B.V. All rights reserved
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