tailieunhanh - Báo cáo hóa học: " An Evolutionary Approach for Joint Blind Multichannel Estimation and Order Detection"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: An Evolutionary Approach for Joint Blind Multichannel Estimation and Order Detection | EURASIP Journal on Applied Signal Processing 2003 8 757-765 2003 Hindawi Publishing Corporation An Evolutionary Approach for Joint Blind Multichannel Estimation and Order Detection Chen Fangjiong Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Electronic Engineering South China University of Technology Wushan Guangzhou 510641 China Email eefjchen@ Sam Kwong Department of Computer Science City University of Hong Kong Kowloon Hong Kong Email cssamk@ Wei Gang Department of Electronic Engineering South China University of Technology Wushan Guangzhou 510641 China Email ecgwei@ Received 30 May 2001 and in revised form 28 January 2003 A joint blind order-detection and parameter-estimation algorithm for a single-input multiple-output SIMO channel is presented. Based on the subspace decomposition of the channel output an objective function including channel order and channel parameters is proposed. The problem is resolved by using a specifically designed genetic algorithm GA . In the proposed GA we encode both the channel order and parameters into a single chromosome so they can be estimated simultaneously. Novel GA operators and convergence criteria are used to guarantee correct and high convergence speed. Simulation results show that the proposed GA achieves satisfactory convergence speed and performance. Keywords and phrases genetic algorithms SIMO blind signal identification. 1. INTRODUCTION Many applications in signal processing encounter the problem of blind multichannel identification. Traditional methods of such identification usually apply higher-order statistics techniques. The major problems of these methods are slow convergence and many local optima 1 . Since the original work of Tong et al. 1 2 many lower-order statisticsbased methods have been proposed for blind multichannel identification see 3 and references therein . A common assumption in these methods is that the channel order .