Distributed maximum a posteriori probability estimation of dynamic systems with wireless sensor networks
Distributed maximum a posteriori probability estimation of dynamic systems with wireless sensor networks
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DOI：
10.1109/icassp.2012.6288513
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Felicia Y. Jakubiec;Alejandro Ribeiro
中科院分区：
文献类型：
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作者：
Felicia Y. Jakubiec;Alejandro Ribeiro
本文提出了一种基于无线传感器网络的时变随机信号估计方法。给定连续时间模型，传感器收集噪声观测，并根据由观测采样周期定义的离散时间等效系统产生局部估计。在给定的感兴趣窗口内使用最大后验概率估计器（MAP）进行估计。为了调解从其他传感器的信息的合并，我们引入拉格朗日乘数惩罚相邻的估计之间的分歧。我们表明，由此产生的分布式（D-）MAP算法是能够跟踪动态信号，误差很小。这个错误的特点是在问题常数和消失的采样时间，只要对数似然函数满足平滑条件。
This paper develops a framework for the estimation of a time-varying random signal using a wireless sensor network. Given a continuous time model, sensors collect noisy observations and produce local estimates according to the discrete-time equivalent system defined by the sampling period of observations. Estimation is performed using a maximum a posteriori probability estimator (MAP) within a given window of interest. To mediate the incorporation of information from other sensors we introduce Lagrange multipliers to penalize the disagreement between neighboring estimates. We show that the resulting distributed (D-)MAP algorithm is able to track dynamical signals with a small error. This error is characterized in terms of problem constants and vanishes with the sampling time as long as the log-likelihood function satisfies a smoothness condition.