Abstract The accessibility of accurate location information for operators in mission-critical scenarios would considerably increase their mission success. In order to obtain precise location information, numerous algorithms and technologies have been suggested. These methods and systems show varying performances under different conditions, and with the help of machine learning techniques, their reliability can be enhanced dramatically. In this paper, we overview the state-of-the-art in emerging algorithms and technologies employing cognitive solutions in mission critical localization applications. We compare these algorithms in terms of different localization parameters such as scalability, power consumption, availability, service quality and accuracy. Consequently, this survey will assist researchers who are working in the area of RF-based localization to achieve better performance in mission critical scenarios that can be experienced in smart city applications.

中文翻译：

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面向智慧城市关键任务应用的基于认知 RF 的定位：概述

摘要 在关键任务场景中，操作员可以获得准确的位置信息将大大提高他们的任务成功率。为了获得精确的位置信息，已经提出了许多算法和技术。这些方法和系统在不同条件下表现出不同的性能，在机器学习技术的帮助下，它们的可靠性可以大大提高。在本文中，我们概述了在关键任务本地化应用程序中采用认知解决方案的新兴算法和技术的最新技术。我们根据不同的定位参数（例如可扩展性、功耗、可用性、服务质量和准确性）来比较这些算法。最后，