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首页> 外文会议>Asilomar Conference on Signals, Systems and Computers >Multiple transmitter localization using clustering by likelihood of transmitter proximity
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Multiple transmitter localization using clustering by likelihood of transmitter proximity

机译：通过发射机接近的可能性使用聚类对多个发射机进行定位

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摘要

We propose a measurement clustering method for estimating the locations of multiple radio transmitters based only on received signal strength and a performance analysis approach drawn from a hypothesis testing framework. Measurements are collected at a set of arbitrary locations in the area of interest. Measurements are retained (or discarded) for transmitter localization based on the probability that a transmitter lies within a given neighborhood of the measurement location. Retained measurements are clustered using k-means or a similar approach, and individual transmitter localization is performed in each cluster. The proposed approach makes no assumptions that the measurements are clustered around transmitters; measurement locations may be at a similar distance from several transmitters, complicating the estimation process. The effect of the choice of neighborhood size and measurement threshold is analyzed using a hypothesis testing framework in which the probability of detection, probability of false alarm, and receiver operating characteristic are derived. Simulation results show that, over a wide range of shadowing noise variance, the proposed clustering-based localization achieves performance gains over algorithms that estimate transmitter locations jointly.
机译：我们提出了一种仅基于接收信号强度来估计多个无线电发射机位置的测量聚类方法，以及一种从假设测试框架得出的性能分析方法。在感兴趣区域中的任意位置处收集测量值。基于发射机位于测量位置的给定邻域内的概率，保留（或丢弃）测量以用于发射机定位。使用k均值或类似方法对保留的测量结果进行聚类，并在每个聚类中执行单独的发射机定位。所提出的方法不假设测量值聚集在发射机周围。测量位置可能与数个发射器相距相似的距离，从而使估算过程复杂化。使用假设测试框架分析选择邻域大小和测量阈值的影响，在该框架中得出检测概率，虚警概率和接收器工作特性。仿真结果表明，在较大范围的阴影噪声方差范围内，与基于联合估计发射机位置的算法相比，所提出的基于聚类的定位方法可实现性能提升。

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