基于峰度自然對數(shù)最大化的信號盲分揀算法和盲波束形成
A logarithm-kurtosis based complex algorithm for blind signal extraction and blind beamforming
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摘要: 該文基于峰度自然對數(shù)最大化準則,提出了一種自適應(yīng)一元信號盲分揀算法,提出的算法可以用于一元信號盲分離和進行盲波束形成,與基于峰度值最大化準則的KMA算法相比,收斂速度快,有較強的穩(wěn)健性,將非線性函數(shù)引入學(xué)習(xí)速率的調(diào)節(jié),算法自動選取學(xué)習(xí)步長,避免了人工選取學(xué)習(xí)速率不當(dāng)而導(dǎo)致算法發(fā)散。同時,提出了兩種復(fù)數(shù)抽氣算法,配合一元信號盲分揀算法可以依次分離多個信號源,仿真試驗驗證了算法的有效性。用提出的算法在四元線陣上盲分離兩個水聲信號,結(jié)果發(fā)現(xiàn),一元信號盲分離實現(xiàn)的盲波束形成波束圖與最優(yōu)波束接近。
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關(guān)鍵詞:
- 信號盲分揀; 盲源分離; 盲波束形成; 高階累積量
Abstract: One source blind extraction can be used to blind beamfonning for underwater acoustic arrays. Among existing candidate approaches such as the simple constant modulus Algorithm (CMA), Kurtosis Maximization Algorithm (KMA), etc., KMA can separate both negative and positive kurtosis signals. As KMA is used to separate underwater acoustic signals the convergence rate is low. Present paper applies logarithm of kurtosis to form tho objective function, and proposes a one source blind extraction algorithm based on logaritlnii-kiiirtosis maximization. At the same time, double deflation algorithms are also proposed to separate more signals in turn. In contrast to KMA convergence rate is improved. A nonlinear Function is used in learning so that the algorithm can choose the learning step automatically. Computer simulations verify the proposed algorithm. -
R. Gooch, J. Lundell, The CMA array: an adaptive beamformer for constant modulus signals,Proc, IEEE Int. Conf. Acoust, Speech Signal Process, Tokyo, Japan, Apr. 1986, 2523-2526.[2]Zhi Ding, Tuan Nguyen, Stationary points of a Kurtosis maximization algorithm for blind signal separation and antenna beamforming, IEEE Trans. on Signal Processing, 2000, SP-48(6), 1587-1596.[3]A. Hyvarinen, E. Oja, One unit learning rules for independent component analysis, Advances in Neural Information Processing System 9 (NIPS 96), Boston, MIT Press, 1997, 480-486.[4]S. Amari, A. Cichocki, Adaptive blind signal processing-Neural network approaches, Proc.IEEE, 1998, 86(10), 2026 2048.[5]R. Thawonmas, A. Cichocki, S. Amari, A cascade neural network for blind signal extraction without spurious equilibria, IEICE Trans. Fundamentals, 1998, E81-A(9), 1-14. -
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