Passive radio frequency (PRF)-based indoor positioning systems (IPS) have attracted researchers' attention due to their low price, easy and customizable configuration, and non-invasive design. This paper proposes a PRF-based three-dimensional (3D) indoor positioning system (PIPS), which is able to use signals of opportunity (SoOP) for positioning and also capture a scenario signature. PIPS passively monitors SoOPs containing scenario signatures through a single receiver. Moreover, PIPS leverages the Dynamic Data Driven Applications System (DDDAS) framework to devise and customize the sampling frequency, enabling the system to use the most impacted frequency band as the rated frequency band. Various regression methods within three ensemble learning strategies are used to train and predict the receiver position. The PRF spectrum of 60 positions is collected in the experimental scenario, and three criteria are applied to evaluate the performance of PIPS. Experimental results show that the proposed PIPS possesses the advantages of high accuracy, configurability, and robustness.

本文提出了一种基于无源射频（PRF）的室内三维定位系统（PIPS），利用场景签名信号进行被动式监测，利用动态数据驱动应用系统（DDDAS）框架进行定制采样频率，通过三种集成学习策略中的各种回归方法进行培训和预测接收机位置。实验结果表明，所提出的PIPS具有高精度、易配置性和鲁棒性的优点。

基于无源射频的集成学习室内3D定位系统