Institute for Information Technology, Technische Universität Ilmenau, P.O. Box 100565, D-98684 Ilmenau, Germany.
Sensors (Basel). 2018 Nov 21;18(11):4073. doi: 10.3390/s18114073.
A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Response estimation from the real signal received by each sensor. Conventional localization techniques mitigate errors trying to avoid NLOS measurements in processing emitter position, while the proposed method uses the multipath fingerprint information produced by ray tracing (RT) simulation together with calibration emitters to refine a Machine Learning engine, which gives an extra layer of information to improve the emitter position estimation. The ray-tracing fingerprints perform the target localization embedding all the reflection and diffraction in the propagation scenario. A validation campaign was performed and showed the feasibility of the proposed method, provided that the buildings can be appropriately included in the scenario description.
提出了一种混合技术来增强在非视距 (NLOS) 郊区场景中部署的到达时间差 (TDOA) 的定位性能。该想法是在数据集上使用机器学习框架，该数据集由射线追踪模拟产生，以及从每个传感器接收到的实际信号的信道冲激响应估计。传统的定位技术通过尝试在处理发射器位置时避免 NLOS 测量来减轻误差，而所提出的方法则使用射线追踪 (RT) 模拟产生的多径指纹信息以及校准发射器来改进机器学习引擎，从而为改善发射器位置估计提供了额外的信息层。射线追踪指纹通过在传播场景中嵌入所有反射和衍射来执行目标定位。进行了验证活动，证明了所提出的方法的可行性，前提是建筑物可以在场景描述中适当包含。
