Selection of Sensors for Efficient Transmitter Localization
Selection of Sensors for Efficient Transmitter Localization
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DOI：
10.1109/infocom41043.2020.9155230
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
A. Bhattacharya;Caitao Zhan;Himanshu Gupta;Samir R Das;P. Djurić
中科院分区：
文献类型：
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作者：
A. Bhattacharya;Caitao Zhan;Himanshu Gupta;Samir R Das;P. Djurić
我们使用一组分布式传感器解决了定位（非法）发射器的问题。我们的重点是开发以有效方式执行发射器定位的技术，其中效率是根据用于定位的传感器的数量来定义的。非法发射机的定位是许多重要应用中出现的一个重要问题，例如，在为任何未经授权的用户巡逻共享频谱系统时。发射机的定位通常是基于一组资源有限的已部署传感器的观测来完成的，因此必须设计最大限度地减少传感器能源资源的技术。在本文中，我们为选择给定数量的传感器的优化问题设计了贪心近似算法，以便最大化适当定义的定位精度目标函数。明显的贪婪算法仅针对两个假设（潜在位置）的特殊情况提供常数因子近似。对于多重假设的一般情况，我们设计了一种基于适当辅助目标函数的贪心算法，并表明它为一般情况提供了可证明的近似解。我们开发了一些技术，通过结合某些观察结果和合理的假设，显着降低所设计算法的时间复杂度。我们在多个模拟平台（包括室内和室外测试台）上评估我们的技术，并证明我们设计的技术的有效性——在大规模模拟中，我们的技术轻松超越先前和其他方法高达 50-60%。
We address the problem of localizing an (illegal) transmitter using a distributed set of sensors. Our focus is on developing techniques that perform the transmitter localization in an efficient manner, wherein the efficiency is defined in terms of the number of sensors used to localize. Localization of illegal transmitters is an important problem which arises in many important applications, e.g., in patrolling of shared spectrum systems for any unauthorized users. Localization of transmitters is generally done based on observations from a deployed set of sensors with limited resources, thus it is imperative to design techniques that minimize the sensors' energy resources.In this paper, we design greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy. The obvious greedy algorithm delivers a constant-factor approximation only for the special case of two hypotheses (potential locations). For the general case of multiple hypotheses, we design a greedy algorithm based on an appropriate auxiliary objective function—and show that it delivers a provably approximate solution for the general case. We develop techniques to significantly reduce the time complexity of the designed algorithms, by incorporating certain observations and reasonable assumptions. We evaluate our techniques over multiple simulation platforms, including an indoor as well as an outdoor testbed, and demonstrate the effectiveness of our designed techniques—our techniques easily outperform prior and other approaches by up to 50-60% in large-scale simulations.