智能手机传感器之间的相关性，算法，和相关技术是在没有通信和定位标准的情况下促进室内定位和跟踪的主要组成部分。在解释这些组件之间的联系以阐明用于室内定位和跟踪的模型的影响和问题方面，可以注意到一个主要的研究差距。在本文中,我们全面研究智能手机传感器，算法，以及可以支持室内定位和跟踪的技术，而不需要任何额外的硬件或特定的基础设施。评论和比较详细说明了每个组件的优点和局限性，在这之后,我们提出了具有零基础设施的基于手持设备的室内定位(HDIZI)方法,以平衡的方式连接上述组件。传感器是输入源，虽然算法以最佳方式用作引擎，为了产生一个强大的定位和跟踪模型，而不需要任何进一步的基础设施。所提出的框架使室内和室外导航更加用户友好，并且对于在手持设备中使用嵌入式传感器的研究人员来说具有成本效益，为工业4.0及以后的技术提供支持。我们使用从两个不同站点收集的数据与五个智能手机作为初始工作进行了实验。数据在固定位置以10Hz的频率采样，持续时间为5秒；此外，数据也是在移动时收集的，允许基于用户跨多个路径的步进行为和速度进行分析。我们利用了智能手机的功能，通过高效的实现和算法的优化集成，为了克服固有的局限性。因此，拟议的HDIZI预计将优于以前研究中提出的方法，帮助研究人员处理传感器，用于室内导航——无论是定位还是跟踪——用于各个领域，比如医疗保健，交通运输,环境监测,或灾难情况。
The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations; furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation-in terms of either positioning or tracking-for use in various fields, such as healthcare, transportation, environmental monitoring, or disaster situations.
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