• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Automatic Target Recognition XXV >Distributed Estimation of a Parametric Field with Random Sensor Placements
【24h】

Distributed Estimation of a Parametric Field with Random Sensor Placements

机译：带有随机传感器放置的参数场的分布式估计

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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：当传感器位置被建模为高斯随机变量时，本文考虑了分布函数估计的问题。我们考虑这样一种场景：传感器部署在具有先验已知的集群中心的集群中（或通过高性能GPS估算），并且传感器在集群中心周围的平均二次分布也已知。分布式传感器对由感兴趣的物理对象生成的未知参数场（例如，由铁质对象生成并由磁力计网络感测的磁场）进行嘈杂的观察。然后，每个传感器都会对其嘈杂的观测结果进行本地信号处理，然后通过并行信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），该并行信道会受到衰落和附加噪声的破坏。中央处理器组合接收到的信号集以形成未知参数场的估计。在我们的数值分析中，我们涉及一个形状为高斯钟形的场。我们尝试使用传感器簇的大小及其数量。场的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度量。可以证明，仅使用少量簇就可以获得相对较好的场估计。随着聚类数目的增加，估计性能稳步提高。结果还表明，在整个网络规模相同的情况下，平均而言，群集数量比每个群集的传感器数量对性能的影响更大。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号