Blind Deconvolution via Sequential Imputations
提出一种顺序插值方法，用于在未知信道和噪声下实时重建离散输入信号，解决了数字通信中的盲反卷积问题。
Abstract The sequential imputation procedure is applied to adaptively and sequentially reconstruct discrete input signals that are blurred by an unknown linear moving average channel and contaminated by additive Gaussian noises, a problem known as blind deconvolution in digital communication. A rejuvenation procedure for improving the efficiency of sequential imputation is introduced and theoretically justified. The proposed method does not require the channel to be nonminimum phase and can be used in real time signal restoration. Two simulated systems are studied to illustrate the proposed method. Our result shows that the ideas of multiple imputations and flexible simulation techniques are as powerful in engineering as in survey sampling. Key Words: Bayesian modelCommunicationGibbs samplingImportance samplingPredictive distributionSignal transmission