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Towards Simulating Multipath Interference at Detectors A Tool for Validating Location Fingerprinting Methods

机译:在检测器上模拟多径干扰验证位置指纹方法的工具

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Signal attributes such as angle of arrival (AoA), time of arrival (ToA), signal amplitude, and phase can be used by a set of receivers (detectors) to perform location fingerprinting (LF), whereby the location of a wireless source is determined. In validating new approaches for location fingerprinting, it is useful to simulate these attributes for the subset of signals that intersect detectors. However, given indoor settings with a complex architecture, it is computationally expensive to simulate multipath propagation while preserving detailed signal information. Moreover, this cost can be unnecessary since determining whether an LF approach is promising may not require tracing all rays that impact the detector. Here, we report on our preliminary efforts to design and test a MATLAB-based simulation tool for wireless propagation that addresses this issue. Our approach builds upon well-known ray-tracing techniques, but innovates via an algorithm designed to obtain a sizable subset of rays that intersect a detector, along with the AoA, ToA, signal amplitude, and phase for each such ray. Finally, we employ our tool in conjunction with a neural network-based method for location fingerprinting, demonstrating the intended use case for our simulation tool.
机译:一组接收器(检测器)可以使用诸如到达角(AoA),到达时间(ToA),信号幅度和相位之类的信号属性来执行位置指纹识别(LF),从而将无线源的位置确定为决定。在验证用于位置指纹识别的新方法时,模拟与检测器相交的信号子集的这些属性非常有用。但是,在室内环境复杂的给定环境下,模拟多径传播同时保留详细的信号信息在计算上是昂贵的。此外,此成本可能是不必要的,因为确定LF方法是否很有希望,可能不需要跟踪影响探测器的所有光线。在这里,我们报告了为解决此问题而设计和测试基于MATLAB的无线传播仿真工具的初步工作。我们的方法以众所周知的光线跟踪技术为基础,但是通过一种算法进行了创新,该算法旨在获得与检测器相交的相当大的光线子集,以及每个此类光线的AoA,ToA,信号幅度和相位。最后,我们将我们的工具与基于神经网络的方法结合使用来进行位置指纹识别,展示了我们的仿真工具的预期用例。

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