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Distributed Estimation of a Parametric Field with Random Sensor Placements

机译：随机传感器放置的参数估计分布估计

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This paper considers a problem of distributed function estimation in the case when sensor locations are modeled as Gaussian random variables. We consider a scenario where sensors are deployed in clusters with cluster centers known a priori (or estimated by a high performance GPS) and the average quadratic spread of sensors around the cluster center also known. Distributed sensors make noisy observations about an unknown parametric field generated by a physical object of interest (for example, magnetic field generated by a ferrous object and sensed by a network of magnetometers). Each sensor then performs local signal processing of its noisy observation and sends it to a central processor (called fusion center) in the wireless sensor network over parallel channels corrupted by fading and additive noise. The central processor combines the set of received signals to form an estimate of the unknown parametric field. In our numerical analysis, we involve a field shaped as a Gaussian bell. We experiment with the size of sensor clusters and with their number. A mean square error between the estimated parameters of the field and the true parameters used in simulations is involved as a performance measure. It can be shown that a relatively good estimate of the field can be obtained with only a small number of clusters. As the number of clusters increases, the estimation performance steadily improves. The results also indicate that, on the average, the number of clusters has more impact on the performance than the number of sensors per cluster, given the same size of the total network.
机译：本文考虑了在传感器位置被建模为高斯随机变量的情况下的分布式函数估计问题。我们考虑一个场景，其中传感器在具有集群中心的集群中部署，已知先验（或通过高性能GPS估计）和群集中心周围的传感器的平均二次传播。分布式传感器对由感兴趣的物理对象产生的未知参数场（例如，由铁物体产生的磁场和由磁力计的网络感测）产生嘈杂的观察。然后，每个传感器执行局部信号处理其嘈杂的观察，并在无线传感器网络中通过衰落和附加噪声损坏的并行通道将其发送到无线传感器网络中的中央处理器（称为融合中心）。中央处理器组合了所接收信号集以形成未知参数字段的估计。在我们的数值分析中，我们涉及一个雕刻为高斯铃的田地。我们尝试传感器集群的大小，并以其数量为单位。估计参数与模拟中使用的真实参数之间的均方误差作为性能测量。可以表明，只有少量簇可以获得对该字段的相对较好的估计。随着群集的数量增加，估计性能稳步提高。结果还表明，在平均值的情况下，群集数量对性能的影响比每个群集的传感器数量更多，给出了总网络的相同大小。

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