Traditionally optimum-adaptive beamforming algorithms have been developed assuming fully coherent plane wavefronts, i.e., assuming a data model of point sources. In most applications this assumption is inappropriate, ...
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Traditionally optimum-adaptive beamforming algorithms have been developed assuming fully coherent plane wavefronts, i.e., assuming a data model of point sources. In most applications this assumption is inappropriate, since the channel model has to account for different kinds of dispersion phenomena due to both the propagation environment and the array itself. Significant examples are sonar and underwater communication systems. Indeed, in such circumstances, the resulting wavefronts can be randomly distorted, usually suffering a loss of spatial coherence. Here, assuming a more realistic stochastic channel model, we analyze the performance of a traditional optimum adaptive beamformer for point sources, when the signal or the interference undergo a spatial coherence degradation. It is shown, with analytical details, that the same coherence loss, for the interference results in larger performance degradation than for the signal, Furthermore, we provide a theoretical comparison among different beamforming algorithms, based on the estimate of the channel parameters and on spatial smoothing methods.
The feasibility of using nontraditional methods of helicopter transmission fault classification is studied. Various signalprocessing and classifier techniques are investigated. All algorithms successfully classified ...
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The feasibility of using nontraditional methods of helicopter transmission fault classification is studied. Various signalprocessing and classifier techniques are investigated. All algorithms successfully classified the fault samples from a tail rotor transmission dataset. A hardware neural network system was designed and implemented. The relatively low resolution of the neural network circuitry required extensive preprocessing and scaling of the large dynamic range input signal. Results from the hardware system were similar to those achieved in simulation. It is pointed out that a true test of the techniques presented may require a dataset that is statistically richer.< >
The 2-D discrete blind deconvolution problem is to reconstruct an image defined at integer coordinates and having known finite spatial extent from its 2-D convolution with a similar and also-unknown point-spread funct...
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The 2-D discrete blind deconvolution problem is to reconstruct an image defined at integer coordinates and having known finite spatial extent from its 2-D convolution with a similar and also-unknown point-spread function. This is significantly more difficult than the typical image restoration problem of deconvolving a known blurring function. Many methods for solving this problem are iterative and alternate between the spatial and wavenumber domains. These algorithms are not POCS and they tend to stagnate. We present a completely novel approach that uses a battery of digital signal processing algorithms to produce an algorithm that comes close to expressing the solution in closed form. The only requirement on the image and point-spread functions are that they each have finite spatial extent and be roughly bandlimited, so that a fractional spatial shift also has finite spatial extent (this requirement is almost always met in practice).
This paper provides array signalprocessing super-resolution fast algorithm. This algorithm is based on the eigenvalue shift of the covariance matrix and the power iteration of the shift covariance matrix. It does not...
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This paper provides array signalprocessing super-resolution fast algorithm. This algorithm is based on the eigenvalue shift of the covariance matrix and the power iteration of the shift covariance matrix. It does not need the inverse matrix. It converges quickly and only needs several iterations to converge to solution. Its architecture is very simple and easily implemented.
In the paper, a parametric Fourier series based model (FSBM) for or as an approximation to an arbitrary nonminimum-phase linear time-invariant (LTI) system is proposed for statistical signalprocessing applications wh...
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ISBN:
(纸本)0818680059
In the paper, a parametric Fourier series based model (FSBM) for or as an approximation to an arbitrary nonminimum-phase linear time-invariant (LTI) system is proposed for statistical signalprocessing applications where a model for LTI systems is needed. Based on the FSBM, a (minimum-phase) linear prediction error (LPE) filter for amplitude estimation of the unknown LTI system together with the Cramer Rao (CR) bounds is presented. Then an iterative algorithm for obtaining the optimum mean-square LPE filter with finite data is presented which is also an approximate maximum likelihood algorithm when the data are Gaussian. Then three iterative algorithms using higher-order statistics with finite non-Gaussian data are presented for estimating parameters of the FSBM followed by some simulation results to support the efficacy of the proposed algorithms. Finally, we draw some conclusions.
Efforts are continuously in hand in the world to improve upon the performance of the SAR interferometric signalprocessing. The quality of the InSAR products has been improved drastically in the last few years. Howeve...
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Efforts are continuously in hand in the world to improve upon the performance of the SAR interferometric signalprocessing. The quality of the InSAR products has been improved drastically in the last few years. However, in order to enhance the accuracy of the InSAR products and obtain results close to the real parameters further tuning of the InSAR processing is needed. In an innovative way, in this paper error evaluation at every step in the InSAR signalprocessing has been carried out and small algorithms have been incorporated to inverse the errors, thus fine tuning the InSAR signalprocessing. Methods and relationships have been developed to enable retrieve the accurate results as much as possible. The relevant processing steps have been identified and recommendations have been made to implement the results with better understanding of different InSAR applications. The necessary steps involved in processing the received data to the geometrically compensated digital models have been explained in an easy and comprehensible way
Digital signal Processors (DSP) are vital system components in industrial Artificial Intelligence (AI) applications. In this paper, FIR filters that could be used for industrial AI applications are designed from the S...
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ISBN:
(纸本)9781538695562;9781538660843
Digital signal Processors (DSP) are vital system components in industrial Artificial Intelligence (AI) applications. In this paper, FIR filters that could be used for industrial AI applications are designed from the Spline Biorthogonal 1.5 (SB1.5) mother wavelet using a real-time, low-cost, generic industrial IoT (IIoT) hardware: the C28x DSP and low-level, Embedded C, system software. Our contribution in this paper is the first reported application of the C28x for SB1.5 wavelet construction. The significance of this approach is to be able to repurpose low-cost, readily available hardware for distributed AI applications. Our approach is different from the state of the art, in which specialized hardware are always manufactured for implementing AI applications at large network edges. Our approach supports low-cost and fast single-stage FIR implementation suitable for use in real-time, distributed AI application at network edges, since in our case, successive recursion of FIR filters leading to a full implementation of Pyramid Algorithm is not implemented. The designed FIR filter is evaluated and found capable of both low-pass and high pass filtering operations. Results of this paper indicate that the C28x real-time DSP, which exists in many IoT devices, could have improved scalability by being deployed for other important AI and IoT network edge analytic applications, different from its present uses.
With the availability of many low-cost programmable development kits in the market, real-time signalprocessing projects can now be readily introduced into today's signalprocessing and embedded system course curr...
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ISBN:
(纸本)9781479999897
With the availability of many low-cost programmable development kits in the market, real-time signalprocessing projects can now be readily introduced into today's signalprocessing and embedded system course curriculum. In this paper, we group these popular development kits in terms of their cost, hardware architecture, development methodology and software resource. We further illustrate the programming efforts in implementing a real-time digital signalprocessing algorithm using different types of programmable development kits.
Convergence properties of the constrained and the unconstrained frequency-domain block LMS algorithms are analyzed, Comparisons based on both a theoretical analysis and computer simulation are given, It is shown that ...
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Convergence properties of the constrained and the unconstrained frequency-domain block LMS algorithms are analyzed, Comparisons based on both a theoretical analysis and computer simulation are given, It is shown that the unconstrained algorithm has a slower convergence rate and smaller stable range of step size than that of the constrained algorithm.
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