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Distributed algorithms for source localization using quantized sensor readings.

机译:使用量化传感器读数进行源定位的分布式算法。

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摘要

We consider sensor-based distributed source localization applications, where sensors transmit quantized data to a fusion node, which then produces an estimate of the source location. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to attain a certain source localization accuracy. We propose an iterative quantizer design algorithm that allows us to take into account the localization accuracy for quantizer design. We show that the quantizer design should be "application-specific" and a new metric should be defined to design such quantizers. In addition, we address, using the generalized BFOS algorithm, the problem of allocating rates to each sensor so as to minimize the error in estimating the position of a source.; We also propose a distributed encoding algorithm that is applied after quantization and achieves significant rate savings by merging quantization bins. The bin-merging technique exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection.; We apply these algorithms to a system where an acoustic amplitude sensor model is employed at each sensor for source localization. For this case, we propose a distributed source localization algorithm based on the maximum a posteriori (MAP) criterion. If the source signal energy is known, each quantized sensor reading corresponds to a region in which the source can be located. Aggregating the information obtained from multiple sensors corresponds to generating intersections between the regions. We develop algorithms that estimate the likelihood of each of the intersection regions. This likelihood can incorporate uncertainty about the source signal energy as well as measurement noise. We show that the computational complexity of the algorithm can be significantly reduced by taking into account the correlation of the received quantized data.; Our simulations show the improved performance of our quantizer over traditional quantizer designs and that our localization algorithm achieves good performance with reasonable complexity as compared to minimum mean square error (MMSE) estimation. They also show that an optimized rate allocation leads to significant rate savings (e.g., over 60%) with respect to a rate allocation that uses the same rate for each sensor, with no penalty in localization efficiency. In addition, they demonstrate rate savings (e.g., over 30%, 5 nodes, 4 bits per node) when our novel bin-merging algorithms are used.
机译:我们考虑基于传感器的分布式源定位应用程序,其中传感器将量化数据传输到融合节点,然后融合节点生成源位置的估计值。对于此应用程序,目标是使传感器节点必须交换的信息量最小化,以便获得一定的源定位精度。我们提出了一种迭代量化器设计算法,该算法可让我们考虑量化器设计的定位精度。我们表明量化器设计应该是“特定于应用程序的”,并且应该定义新的度量标准来设计这种量化器。此外,我们使用广义BFOS算法解决了将速率分配给每个传感器的问题,从而最大程度地减少了估计源位置时的误差。我们还提出了一种分布式编码算法,该算法在量化后应用,并通过合并量化仓来显着节省速率。箱合并技术利用了以下事实:由于对应的空间区域具有空的相交,所以在每个节点处不会出现量化箱的某些组合。我们将这些算法应用于系统,其中在每个传感器处采用声振幅传感器模型进行源定位。对于这种情况,我们提出了一种基于最大后验(MAP)准则的分布式源定位算法。如果源信号能量已知,则每个量化的传感器读数都对应于可以放置源的区域。汇总从多个传感器获得的信息对应于在区域之间生成交集。我们开发了估计每个交叉区域可能性的算法。这种可能性可能包含有关源信号能量以及测量噪声的不确定性。我们表明,通过考虑接收到的量化数据的相关性,可以显着降低算法的计算复杂度。我们的仿真表明,与传统的量化器设计相比,量化器的性能有所提高,并且与最小均方误差(MMSE)估计相比,我们的定位算法以合理的复杂度实现了良好的性能。他们还表明,相对于为每个传感器使用相同速率的速率分配,优化的速率分配可显着节省速率(例如,节省超过60%),而不会降低定位效率。另外,当使用我们新颖的bin合并算法时,它们证明了速率的节省(例如,超过30%,5个节点,每个节点4位)。

著录项

  • 作者

    Kim, Yoon Hak.;

  • 作者单位

    University of Southern California.$bElectrical Engineering: Doctor of Philosophy.;

  • 授予单位 University of Southern California.$bElectrical Engineering: Doctor of Philosophy.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 109 p.
  • 总页数 109
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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