Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models
Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models
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DOI：
10.1109/tsp.2006.877659
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发表时间：
2006-08
影响因子：
5.4
通讯作者：
Aleksandar Dogandzic;Benhong Zhang
中科院分区：
文献类型：
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作者：
Aleksandar Dogandzic;Benhong Zhang
我们为传感器网络环境中的分布式信号处理开发一个隐藏的马尔可夫随机字段（HMRF）框架。在此框架下，在传感器上收集的空间分布的观测值形成了一个基础随机场的嘈杂实现，其具有简单的结构具有马尔可夫依赖性。我们从嘈杂的测量值中得出了迭代的条件模式（ICM）算法，以对隐藏的随机场进行分布式估计。我们考虑参数和非参数测量模型。所提出的分布式估计器在计算上是简单的，适用于广泛的感应环境，并局部化，这意味着节点仅与邻居进行通信以获得所需的结果。我们还开发了一种校准方法，用于从训练数据中估算Markov随机场模型参数，并讨论ICM算法的初始化。 HMRF框架和ICM算法应用于事件区域检测。数值模拟证明了提出方法的性能
We develop a hidden Markov random field (HMRF) framework for distributed signal processing in sensor-network environments. Under this framework, spatially distributed observations collected at the sensors form a noisy realization of an underlying random field that has a simple structure with Markovian dependence. We derive iterated conditional modes (ICM) algorithms for distributed estimation of the hidden random field from the noisy measurements. We consider both parametric and nonparametric measurement-error models. The proposed distributed estimators are computationally simple, applicable to a wide range of sensing environments, and localized, implying that the nodes communicate only with their neighbors to obtain the desired results. We also develop a calibration method for estimating Markov random field model parameters from training data and discuss initialization of the ICM algorithms. The HMRF framework and ICM algorithms are applied to event-region detection. Numerical simulations demonstrate the performance of the proposed approach