The normal mode model is assumed to give a fair representation of sound propagation in shallow water. A simulated experiment is conducted that involves a monochromatic source and a vertical a linear array, with the ob...
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ISBN:
(纸本)9781728114507
The normal mode model is assumed to give a fair representation of sound propagation in shallow water. A simulated experiment is conducted that involves a monochromatic source and a vertical a linear array, with the objective of estimating the parameters of this normal mode model. For this purpose, we have the source transmitting at every sensing depth. Collected measurements are stacked in a first data matrix. The range between source and array is modified, the experiment is repeated and a second data matrix is collected. A special combination of the two data matrices shows an interesting eigen structure, where (non -orthogonal) eigenvectors and eigenvalues turn to be the sought-after sampled model functions and wavenumbers. The so -defined subspace algorithm is not based on (orthogonal) singular vectors and, so, does not require full coverage of the water column, unlike existing subspace algorithms. It also compares advantageously to transform domain techniques which, while not requiring full coverage of the water column, involve impulsive sources, among other limitations.
In passive radar, two main challenges are: mitigating the direct blast, since the illuminators broadcast continuously, and achieving a large enough integration gain to detect targets. While the first has to be solved ...
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ISBN:
(纸本)9781424429400
In passive radar, two main challenges are: mitigating the direct blast, since the illuminators broadcast continuously, and achieving a large enough integration gain to detect targets. While the first has to be solved in part in the analog part of the processing chain, due to the huge difference of signal strength between the direct blast and weak target reflections, the second is about combining enough signal efficiently, while not sacrificing too much performance. When combining this setup with digital multicarrier waveforms like orthogonal frequency division multiplex (OFDM) in digital audio/video broadcast (DAB/DVB), this problem can be seen to be a version of multiple-input multiple-output (MIMO) radar. We start with an existing approach, based on efficient fast Fourier transform (FFT) operation to detect target signatures, and show how this approach is related to a standard matched filter approach based on a piece-wise constant approximation of the phase rotation caused by Doppler shift. We then suggest two more applicable algorithms, one based on subspace processing and one based on sparse estimation. We compare these various approaches based on a detailed simulation scenario with two closing targets and experimental data recorded from a DAB network in Germany.
Complex-valued data are encountered in many application areas of signal and image processing. In the context of optimization of functions of real variables, subspace algorithms have recently attracted much interest, d...
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ISBN:
(纸本)9780992862602
Complex-valued data are encountered in many application areas of signal and image processing. In the context of optimization of functions of real variables, subspace algorithms have recently attracted much interest, due to their efficiency in solving large-size problems while simultaneously offering theoretical convergence guarantees. The goal of this paper is to show how some of these methods can be successfully extended to the complex case. More precisely, we investigate the properties of the proposed complex-valued Majorize-Minimize Memory Gradient (3MG) algorithm. An important practical application of these results arises for image reconstruction in Parallel Magnetic Resonance Imaging (PMRI). Comparisons with existing optimization methods confirm the good performance of our approach for PMRI reconstruction.
Stochastic optimization plays an important role in solving many problems encountered in machine learning or adaptive processing. In this context, the second-order statistics of the data are often unknown a priori or t...
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ISBN:
(纸本)9780992862619
Stochastic optimization plays an important role in solving many problems encountered in machine learning or adaptive processing. In this context, the second-order statistics of the data are often unknown a priori or their direct computation is too intensive, and they have to be estimated on-line from the related signals. In the context of batch optimization of an objective function being the sum of a data fidelity term and a penalization (e.g. a sparsity promoting function), Majorize-Minimize (MM) subspace methods have recently attracted much interest since they are fast, highly flexible and effective in ensuring convergence. The goal of this paper is to show how these methods can be successfully extended to the case when the cost function is replaced by a sequence of stochastic approximations of it. Simulation results illustrate the good practical performance of the proposed MM Memory Gradient (3MG) algorithm when applied to 2D filter identification.
This paper considers the joint source channel coding using a new class of DFT codes which we refer to as Hermitian symmetric DFT (HSDFT) codes. A new decoding algorithm for decoding of HSDFT codes is presented. With t...
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ISBN:
(纸本)9781424453092
This paper considers the joint source channel coding using a new class of DFT codes which we refer to as Hermitian symmetric DFT (HSDFT) codes. A new decoding algorithm for decoding of HSDFT codes is presented. With this new decoding algorithm and an appropriate transmission scheme, HSDFT codes are capable of correcting more burst errors than the capacity of any maximum distance separable (MDS) code. Experimental results obtained by transmission of image over binary symmetric channel and Gilbert-Elliot channel are also presented, which show that HSDFT codes perform similar to the existing DFT class of codes on a binary symmetric channel while it performs consistently better (by around 2dB) on a Gilbert-Elliot like channel.
Three automatic model selection procedures are compared: The first one is the pem subroutine implemented in the MATLAB toolbox system identification. The second procedure is based on the CCA subspace procedure. The th...
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Three automatic model selection procedures are compared: The first one is the pem subroutine implemented in the MATLAB toolbox system identification. The second procedure is based on the CCA subspace procedure. The third procedure finally is the ARMAsel procedure. The three procedures have heen studied using a simulation experiment. The accuracy of the subspace algorithms depends heavily on the choice of the integers f and p. Simple strategies for choosing f and p can yield very inaccurate models. For one simulation example, it is shown that a more complex strategy can yield a more accurate result. The ARMAsel algorithm provided an accurate model for all processes that have been considered.
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