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Low-Complexity Addition or Removal of Sensors/Constraints in LCMV Beamformers

机译:LCMV波束形成器中传感器/约束的低复杂性添加或删除

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摘要

We address the application of the linearly constrained minimum variance (LCMV) beamformer in sensor networks. In signal processing applications, it is common to have a redundancy in the number of nodes, fully covering the area of interest. Here we consider suboptimal LCMV beamformers utilizing only a subset of the available sensors for signal enhancement applications. Multiple desired and interfering sources scenarios in multipath environments are considered. We assume that an oracle entity determines the group of sensors participating in the spatial filtering, denoted as the active sensors. The oracle is also responsible for updating the constraints set according to either sensors or sources activity or dynamics. Any update of the active sensors or of the constraints set necessitates recalculation of the beamformer and increases the power consumption. As power consumption is a most valuable resource in sensor networks, it is important to derive efficient update schemes. In this paper, we derive procedures for adding or removing either an active sensor or a constraint from an existing LCMV beamformer. Closed-form, as well as generalized sidelobe canceller (GSC)-form implementations, are derived. These procedures use the previous beamformer to save calculations in the updating process. We analyze the computational burden of the proposed procedures and show that it is much lower than the computational burden of the straightforward calculation of their corresponding beamformers.
机译:我们解决了线性约束最小方差(LCMV)波束形成器在传感器网络中的应用。在信号处理应用中,通常在节点数量上具有冗余,以完全覆盖感兴趣的区域。在这里,我们考虑次佳的LCMV波束形成器,它仅将可用传感器的一个子集用于信号增强应用。考虑了多径环境中的多个期望和干扰源场景。我们假设一个预言实体确定参与空间滤波的传感器组,称为活动传感器。 oracle也负责根据传感器或源活动或动态更新约束集。有源传感器或约束集的任何更新都需要重新计算波束形成器并增加功耗。由于功耗是传感器网络中最有价值的资源,因此获得有效的更新方案非常重要。在本文中,我们推导了从现有LCMV波束形成器中添加或删除有源传感器或约束的过程。得出了封闭形式以及广义旁瓣抵消器(GSC)形式的实现。这些过程使用以前的波束形成器在更新过程中保存计算。我们分析了所提出程序的计算负担,并表明它比直接计算相应波束形成器的计算负担低得多。

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