With the rapid development of Internet of Things (IoT) technology, location-based services have been widely applied in the construction of smart cities. Satellite-based location services have been utilized in outdoor environments, but they are not suitable for indoor technology due to the absence of global positioning system (GPS) signal. Therefore, many indoor localization technologies and systems have emerged by utilizing many other signals. In particular, fingerprinting localization has recently garnered attention because its promising performance. In this work, we aim to study recent indoor localization technologies and systems based on various fingerprints, which use machine learning and intelligent algorithms. We also present the architecture of intelligent localization. The development of indoor localization technology should have the ability of self-adaptation and self-learning in the future. And the architecture shows how to make localization become more “smart” by advanced techniques. The state-of-the-art localization systems’ working principles are summarized and compared in terms of their localization accuracy, latency, energy consumption, complexity, and robustness. We also discuss the challenges of existing indoor localization technologies, potential solutions to these challenges, and possible improvement measures.

中文翻译：

---

室内智能指纹定位：原理、途径与挑战

随着物联网（IoT）技术的快速发展，基于位置的服务在智慧城市建设中得到了广泛的应用。基于卫星的定位服务已在室外环境中使用，但由于没有全球定位系统 (GPS) 信号，它们不适合室内技术。因此，出现了许多利用许多其他信号的室内定位技术和系统。特别是，指纹定位最近因其良好的性能而受到关注。在这项工作中，我们的目标是研究最近使用机器学习和智能算法的基于各种指纹的室内定位技术和系统。我们还介绍了智能定位的架构。未来室内定位技术的发展应该具备自适应和自学习的能力。该架构展示了如何通过先进技术使本地化变得更加“智能”。最先进的定位系统的工作原理在定位精度、延迟、能耗、复杂性和鲁棒性方面进行了总结和比较。我们还讨论了现有室内定位技术的挑战、这些挑战的潜在解决方案以及可能的改进措施。能耗、复杂性和鲁棒性。我们还讨论了现有室内定位技术的挑战、这些挑战的潜在解决方案以及可能的改进措施。能耗、复杂性和鲁棒性。我们还讨论了现有室内定位技术的挑战、这些挑战的潜在解决方案以及可能的改进措施。