An adaptivealgorithm for a single sinusoid detection using IIR bandpass filter with parallel block structure has been proposed by Nishimura et al. However, the algorithm has three problems: First, it has several inpu...
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An adaptivealgorithm for a single sinusoid detection using IIR bandpass filter with parallel block structure has been proposed by Nishimura et al. However, the algorithm has three problems: First, it has several input frequencies being impossible to converge. Secondly, the convergence rate can not be higher than that of the scalar structure. Finally, it has a large amount of computation. In this paper, a new algorithm is proposed to solve these problems. In addition, a new structure is proposed to reduce the amount of computation, in which the adaptive control signal generator is realized by the parallel block structure. Simulation results are given to illustrate the performance of the proposed algorithm.
Three essential improvements are described to the treecode in terms of the expansion formula, the choice of the expansion order as well as the tree structure. Firstly, the multipole expansion is based on the real sphe...
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Three essential improvements are described to the treecode in terms of the expansion formula, the choice of the expansion order as well as the tree structure. Firstly, the multipole expansion is based on the real spherical harmonic functions to reduce the CPU time. Secondly, the expansion order is given in terms of the ratio of the distance of a field point to a source box to the box size, which reflects the relative error of the expansion. With that, a large portion of the sources has been evaluated by the multipole expansion at low levels of the source tree, which is around two-thirds of sources at the first two levels of the tree averagely. The algorithm reduces the CPU time dependency on expansion order p from O(p(2)) of the classical treecode to be lower than a linear dependency in p(max), where p(max) is the maximum expansion order used in the variable order expansion. Thirdly, a revised binary tree is built by performing the bisections thrice at each tree level, discarding the boxes generated in the first two bisections and remaining only the boxes generated in the last one. This tree avoids the disadvantage of a binary treecode demanding significantly more CPU time than an oct-treecode. It has high adaptiveness to the source distribution and perfect load balancing for performing the parallelization. Simulations are carried out for N vortex elements and N field points distributed randomly in a cube, a 5:1:1 parallelepiped, and a 10:1:1 parallelepiped, using the oct-tree and revised binary tree, respectively. The algorithm is an order of magnitude faster than those of Strickland et al. [ESAIM: Proceedings 7 (1999) 408], Warren and Salmon [Comput. Phys. Commun. 87 (1995) 266], and Lindsay and Krasny [J. Comput. Phys. 172 (2001) 879]. Simulations also demonstrate the efficiency of the revised binary treecode for an inhomogeneous source distribution. (C) 2004 Elsevier Inc. All rights reserved.
In this article, a new approach for active sonar-target detection based on a modified version of the method of adaptive noise cancellation (ANC) originally proposed by Widrow et al. (1975) is presented. Widrow's m...
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In this article, a new approach for active sonar-target detection based on a modified version of the method of adaptive noise cancellation (ANC) originally proposed by Widrow et al. (1975) is presented. Widrow's method requires a good estimate of reference noise in order to effectively 'cancel' the noise. A novel method of securing the required reference noise estimates is proposed in this article. The estimated reference noise should be highly correlated with the actual noise for effective cancellation of noise. In order to render this property, a correlating filter, designed according to a suitable optimality criterion, is made use of at the input of adaptive noise canceller. A new fast convergent adaptivealgorithm is derived for updating the weights of the adaptive filter in the adaptive noise canceller. Target detection is effected by comparing the magnitude of the output of the adaptive noise canceller with a decision threshold. Computer simulations are carried out to demonstrate the performance of the proposed modified ANC-based method for target detection. The simulation results demonstrate that the proposed scheme is capable of performing target detection and signal-arrival time estimation with a very high degree of reliability at SNRs as low as -70 dB.
This paper introduces new minor (noise) subspace tracking (MST) algorithms based on the minimum noise subspace (MNS) technique. The latter has been introduced as a computationally efficient subspace method for blind s...
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ISBN:
(纸本)9781467300469
This paper introduces new minor (noise) subspace tracking (MST) algorithms based on the minimum noise subspace (MNS) technique. The latter has been introduced as a computationally efficient subspace method for blind system identification. We exploit here the principle of the MNS, to derive the most efficient algorithms for MST. The proposed method joins the advantages of low complexity and fast convergence rate. Moreover, this method is highly parallelizable and hence its computational cost can be easily reduced to a very low level when parallel architectures are available. Different implementations are proposed for different contexts and they are compared via numerical simulations.
In this paper,a new adaptivealgorithm of high speed adaptive noise canceller with parallel block structure is ***,an IIR bandpass filter with a variable center angular frequency using adaptive Q-factor control is sho...
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In this paper,a new adaptivealgorithm of high speed adaptive noise canceller with parallel block structure is ***,an IIR bandpass filter with a variable center angular frequency using adaptive Q-factor control is shown in which the bandpass filter and the adaptive control signal generator are composed of parallel block ***,if we use a large step size parameter of the adaptive updating algorithm for fast processing,the large step size parameter may cause a large variation after convergence of the adaptive *** order to reduce the variation after convergence of the adaptivealgorithm,we propose a new adaptive updating algorithm in which is used the input for *** method has essentially the same convergence performance as the previous *** the variation after convergence of the new adaptive updating algorithm is always smaller than the one of the previous *** new algorithm has a small increment in computational ***,the simulation results are given to demonstrate the convergence performance.
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