Current Issue : October - December Volume : 2014 Issue Number : 4 Articles : 5 Articles
We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy electroencephalographic (EEG)\nmeasurements, commonly named as the EEG inverse problem. We propose a new method to induce\nneurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity, and low rank\nof the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix\nand a dense latent source matrix. The structured sparse-low-rank structure is enforced by minimizing a regularized\nfunctional that includes the 21-norm of the coding matrix and the squared Frobenius norm of the latent source\nmatrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization\nproblem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic\nscenarios, the performance of our method with respect to the Group Lasso and Trace Norm regularizers when they\nare applied directly to the target matrix....
The conventional global positioning system (GPS) can often fail to provide position determination for a mobile user\nin indoor and urban environments. To cope with GPS failure in such environments, a new navigation system which\nutilizes a terrestrial digital multimedia broadcasting (T-DMB) signal to obtain the mobile user's position is presented.\nSince the T-DMB transmitters in Korea construct a single frequency network (SFN), which forces the transmitters to\nbe synchronized, the mobile user can measure a time difference of arrival (TDOA) for all audible T-DMB transmitter\npairs. The time difference between T-DMB transmitters is converted to a distance difference by multiplying the time\ndifference by the speed of light. Using these measurements and a TDOA positioning method, the mobile user\nposition can be estimated. An experiment with a T-DMB receiver and a data acquisition (DAQ) board is performed\nin Seoul to analyze the error characteristic of TDOA measurements. It is certified that the measurement error is\nbounded under 300 m and can be used to determine the mobile user's position with a small standard deviation....
In this paper, a third-order moment-based estimation of signal parameters via rotational invariance techniques\n(ESPRIT) algorithm is proposed for passive localization of near-field sources. By properly choosing sensor outputs of\nthe symmetric uniform linear array, two special third-order moment matrices are constructed, in which the steering\nmatrix is the function of electric angle ? , while the rotational factor is the function of electric angles ? and ?. With\nthe singular value decomposition (SVD) operation, all direction-of-arrivals (DOAs) are estimated from a polynomial\nrooting version. After substituting the DOA information into the steering matrix, the rotational factor is determined\nvia the total least squares (TLS) version, and the related range estimations are performed. Compared with the\nhigh-order ESPRIT method, the proposed algorithm requires a lower computational burden, and it avoids the\nparameter-match procedure. Computer simulations are carried out to demonstrate the performance of the\nproposed algorithm....
Recently with an emerging theory of ââ?¬Ë?compressive sensingââ?¬â?¢ (CS), a radically new concept of compressive sensing radar\n(CSR) has been proposed in which the time-frequency plane is discretized into a grid. Random filtering is an\ninteresting technique for efficiently acquiring signals in CS theory and can be seen as a linear time-invariant filter\nfollowed by decimation. In this paper, random filtering structure-based CSR system is investigated. Note that the\nsparse representation and sensing matrices are required to be as incoherent as possible; the methods for optimizing\nthe transmit waveform and the FIR filter in the sensing matrix separately and simultaneously are presented to\ndecrease the coherence between different target responses. Simulation results show that our optimized results lead\nto smaller coherence, with higher sparsity and better recovery accuracy observed in the CSR system than the\nnonoptimized transmit waveform and sensing matrix...
It is known that distributed beamforming techniques can improve the performance of relay networks by using\nchannel state information (CSI). In practical applications, there exist unavoidably estimation errors of the CSI, which\nresults in outage of quality of service (QoS) or overconsumption of transmit power. In this paper, we propose two\nworst-case-based distributed beamforming techniques that are robust to the channel estimation errors. In the\nworst-case-based approaches, the worst case in a set that includes the actual case is optimized. Therefore, the\nperformance of the actual case can be guaranteed. In our first approach, the maximal total relay transmit power in the\nset is minimized subject to the QoS constraint. This distributed beamforming problem can be approximately solved\nusing second-order cone programming (SOCP). In our second method, the worst QoS in the set is maximized subject\nto the constraints of individual relay transmit powers. It is shown that the resultant problem can be approximately\nformulated as a quasi-convex problem and can be solved by using a bisection search method. Simulation results\nshow that the proposed beamforming techniques are robust to the CSI errors and there is no outage of QoS or power\nin the proposed methods....
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