首页> 外文会议>IEEE Military Communications Conference >OPTIMAL NODE PLACEMENT IN DECISION FUSION WIRELESS SENSOR NETWORKS FOR DISTRIBUTED DETECTION OF A RANDOMLY-LOCATED TARGET
【24h】

OPTIMAL NODE PLACEMENT IN DECISION FUSION WIRELESS SENSOR NETWORKS FOR DISTRIBUTED DETECTION OF A RANDOMLY-LOCATED TARGET

机译:决策融合无线传感器网络的最佳节点放置用于随机定位目标的分布式检测

获取原文

摘要

We consider the problem of optimal (fixed) wireless sensor network (WSN) design for distributed detection of a randomly-located target. A distributed one-dimensional (1-D) WSN model with equal spacing d between any two adjacent nodes is assumed We first model the target as being randomly located following an exponential distribution with a known parameter, and the channel between the nodes and the fusion center to be AWGN with path loss attenuation. A simplified decision fusion rule for the high observation signal-to-noise ratio (SNR) regime and its Bayesian error probability are derived, which then is used to optimize the parameters of the WSN. The optimal sensor placements are obtained in the limit of a large sensor system and the analytical properties of the obtained solution are discussed with corresponding numerical examples. It is shown that in many cases deviation from optimal inter-node spacing can cost significant performance penalty. Finally, the results are generalized to a fading wireless channel and for any target-location distribution specified only via its second-order statistics. It is shown that the optimal sensor placements essentially stays the same regardless of whether the channel is AWGN or fading, and are insensitive to operating SNR's either at the fusion center or at the local sensor nodes.
机译:我们考虑用于随机定位目标的分布式检测的最佳(固定)无线传感器网络(WSN)设计的问题。假设任何两个相邻节点之间的等于间隔D的分布式一维(1-D)WSN模型我们首先将目标模型为随机位于具有已知参数的指数分布之后,以及节点和融合之间的信道中心为aggn,路径损失衰减。推导出高观察到信噪比(SNR)制度和其贝叶斯误差概率的简化决策融合规则,然后用于优化WSN的参数。在大传感器系统的极限中获得最佳传感器放置,并且通过相应的数值示例讨论所得溶液的分析性质。结果表明,在许多情况下,从最佳节点间距偏差可以成本显着的性能损失。最后,结果是推广到衰落的无线信道,并且仅通过其二阶统计指定的任何目标位置分布。结果表明,无论频道是否为AWGN或衰落,最佳传感器放置基本上都保持不变,并且对融合中心或本地传感器节点的操作SNR不敏感。

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