自适应分布式传感器网络中增量式LMS算法的跟踪性能
Tracking Performance of Incremental LMS Algorithm over Adaptive Distributed Sensor Networks
摘要：
本文主要研究增量式自适应LMS算法在自适应网络中的跟踪性能。由于这个原因，我们认为未知的权重向量是一个时变序列。首先分析了网络跟踪时变权向量的性能，然后通过随机游走模型解释了瑞利衰落信道的估计。在瑞利衰落信道和随机游走模型下，推导了所分析网络的均方误差（MSE）、均方偏差（MSD）和超额均方误差（EMSE）的闭合关系。理论和模拟结果之间的比较显示了完美的匹配，并验证了所执行的计算。
英文摘要：
In this paper we focus on the tracking performance of incremental adaptive LMS algorithm in an adaptive network. For this reason we consider the unknown weight vector to be a time varying sequence. First we analyze the performance of network in tracking a time varying weight vector and then we explain the estimation of Rayleigh fading channel through a random walk model. Closed-form relations are derived for mean square error (MSE), mean square deviation (MSD) and excess mean square error (EMSE)of analyzed network in tracking Rayleigh fading channel and random walk model. Comparison between theoretical and simulation results shows a perfect match and verifies performed calculations.
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