SIGNAL ESTIMATION WITH BINARY-VALUED SENSORS

作     者：Leyi WANG Gang George YIN Chanying LI Weixing ZHENG Leyi WANG Department of Electrical and Computer Engineering,Wayne State University,Detroit,Michigan 48202,USA. Gang George YIN Department of Mathematics,Wayne State University,Detroit,Michigan 48202,USA. Chanying LI Department of Mechanical Engineering,The University of Hong Kong,Pokfulam Road,Hong Kong,China. Weixing ZHENG School of Computing and Mathematics,University of Western Sydney,Australia.

作者机构：Department of Electrical and Computer Engineering Wayne State University Detroit Michigan 48202 USA Department of Mathematics Wayne State University Detroit Michigan 48202 USA Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong China School of Computing and Mathematics University of Western Sydney Australia 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2010年第23卷第3期

页      面：622-639页

核心收录：

学科分类：07[理学] 080202[工学-机械电子工程] 08[工学] 070104[理学-应用数学] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 0701[理学-数学] 0811[工学-控制科学与工程] 

基　　金：supported in part by the National Science Foundation under ECS-0329597 and DMS-0624849 in part by the Air Force Office of Scientific Research under FA9550-10-1-0210 supported by the National Science Foundation under DMS-0907753 and DMS-0624849 in part by the Air Force Office of Scientific Research under FA9550-10-1-0210 supported in part by a research grant from the Australian Research Council 

主　　题：Identification signal estimation. 

摘      要：This paper introduces several algorithms for signal estimation using binary-valued *** main idea is derived from the empirical measure approach for quantized identification,which has been shown to be convergent and asymptotically efficient when the unknown parametersare *** estimation under binary-valued observations must take into consideration oftime varying *** empirical measure based algorithms are modified with exponentialweighting and threshold adaptation to accommodate time-varying natures of the *** anyinformation on signal generators,the authors establish estimation algorithms,interaction between noisereduction by averaging and signal tracking,convergence rates,and asymptotic efficiency.A thresholdadaptation algorithm is *** convergence and convergence rates are analyzed by using theODE method for stochastic approximation problems.