Multiple Transmitter Localization under Time-Skewed Observations
Multiple Transmitter Localization under Time-Skewed Observations
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DOI：
10.1109/dyspan.2019.8935739
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Mohammad Ghaderibaneh;Mallesham Dasari;Himanshu Gupta
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文献类型：
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作者：
Mohammad Ghaderibaneh;Mallesham Dasari;Himanshu Gupta
无线电频谱是一种有限的自然资源，需求量很大，因此必须有效监测和保护，防止未经授权的访问。最近，人们对使用廉价的商品级频谱传感器进行大规模RF频谱监测产生了极大的兴趣。这些传感器是廉价的，可以以更高的密度部署，因此，可以提供更准确的频谱占用图或入侵者检测方案。然而，这些传感器是廉价的，也具有有限的计算资源，并且是独立的和分布式的，可能遭受时钟偏斜（即，它们的时钟可能不充分同步）。在本文中，我们感兴趣的多个入侵者同时存在的检测和定位的问题，在上述的背景下，有限的资源和时钟偏差的分布式传感器。使用具有时钟偏差的传感器解决入侵者定位问题的关键挑战是，对于任何（绝对）时刻，甚至很难导出传感器上的观测向量。在这项工作中，我们提出了基于组的算法，倾斜意识的多个入侵者定位方法，基本上是通过提取观测跨传感器的某些小集合的发射机。我们的研究结果表明，基于组的算法产生了显着的提高精度相对简单的方法。
Radio spectrum is a limited natural resource under a significant demand and thus, must be effectively monitored and protected from unauthorized access. Recently, there has been a significant interest in the use of inexpensive commodity-grade spectrum sensors for large-scale RF spectrum monitoring. These sensors being inexpensive can be deployed at much higher density, and thus, can provide much more accurate spectrum occupancy maps or intruder detection schemes. However, these sensors being inexpensive also have limited computing resources, and being independent and distributed can suffer from clock skew (i.e., their clocks may not be sufficiently synchronized). In this paper, we are interested in the problem of detection and localization of multiple intruders present simultaneously, in the above context of distributed sensors with limited resources and clock skew. The key challenge in addressing the intruder localization problem using sensors with clock skew is that it is very difficult to even derive an observation vector over sensors, for any (absolute) instant. In this work, we propose Group-Based Algorithm, a skew-aware multiple intruders localization method that essentially works by extracting observations across sensors for certain small sets of transmitters. Our results show that Group-Based Algorithm yields significant improvement of accuracy over relatively simpler approaches.