基于最大熵化法的衛(wèi)星信號(hào)盲分離
Blind Separation of Satellite Signals Based on the Maximum Entropy
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摘要: 該文研究的問(wèn)題是從經(jīng)過(guò)衛(wèi)星信道的混合信號(hào)中分離出相互獨(dú)立的原始信號(hào)。解決這類(lèi)問(wèn)題的傳統(tǒng)方法往往是采用盲解卷積的算法，但是這種方法的計(jì)算量很大，需要對(duì)各個(gè)徑的參數(shù)進(jìn)行調(diào)整。該文利用衛(wèi)星信道的特點(diǎn)，提出了基于最大熵的盲分離算法，極大地減小了計(jì)算量。最后的仿真結(jié)果表明了算法有效性。
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關(guān)鍵詞:
- 盲分離; 最大熵化法; 預(yù)處理
Abstract: The problem that this paper discusses is how to separate the origin signals from the mixed satellite signals. The traditional method to solve the kind of problem adopts the algorithm of blind convolution which needs much account to adjust the all pathway parameters. However using the characteristic of satellite channel this paper put forwards the blind separation algorithm based on maximum entropy which can deduce the calculation too much.The results of simulations testify the validity . -
Jutten C, Herault J. Blind separation of source , An adaptive algorithm based on neuromimetic architecture[J].Signal Processing.1991, 24(1):1-29[2]Common P. Independent component analysis: a new concept? Signal Processing, 1994, 36(3): 287-314.[3]Amari S. Natural gradient works efficient in learning[J].Neural Computation.1998, 10:251-276[4]譚麗麗, 韋崗. 多輸入多輸出盲解卷問(wèn)題的最大熵化法. 電子學(xué)報(bào), 2000 , 28(1): 114-116.[5]Bell A J, Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution[J].Neural Computation.1995, 7:1129-1159[6]Cardoso J F, Laheld. B H. Equivariant adaptive source separation[J]. IEEE Trans. on Signal Processing, 1996, 43(12): 3017-3030.[7]Aapo Hyvarinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. on Neural Networks , 1999 , 10(3): 626-634. -
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