Intelligent Geomatics Systems for Reliable Positioning in Smart Cities

用于智能城市可靠定位的智能地理信息系统

• 批准号: RGPIN-2021-02803
• 负责人: OKeefe, Kyle
• 金额: $ 2.62万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=755064
• 关键词: Intelligent; Geomatics; Systems; Reliable; Positioning

Global Navigation Satellite Systems (GNSS) provide positioning to a broad range of users operating in outdoors however indoor and urban navigation remains challenging in terms of availability, accuracy and reliability. The majority of GNSS users are now individuals with wireless handsets and that spend the majority of their time in urban and indoor environments. New ground-based wireless communication standards that support direct range measurement, including Ultra-Wideband (UWB), 802.11mc (WiFi) and 5G are emerging while Bluetooth Low Energy (BLE) is becoming the standard for low precision ranging and contact tracing. The objective of this research is to improve indoor and urban navigation to the several centimetres level through integration of GNSS with these ranging systems. The proposed research aims to: 1. Integrate new ranging technologies with GNSS in urban environments. The performance of IEEE 802.11mc and 5G ranging will be investigated and compared with UWB ranging and improved error models for 802.11mc and 5G will be developed. Artificial intelligence will be applied to correct measurement errors and the results integrated with GNSS observations to estimate position and each system's systematic errors. 2. Improve BLE ranging using 802.11mc, 5G and UWB. Low power, low cost, low complexity BLE has been identified as a favoured method for electronic contact tracing in the context of the 2020 Covid-19 pandemic. While BLE can determine if devices have been in proximity, its use for range estimation is limited. Recent work using artificial intelligence techniques to learn to identify changes in BLE observations due to obstructions will be expanded to develop a concept where a subset of devices equipped with the other 3 ranging systems can be used to train low-cost systems that rely only on BLE. 3. Integrate GNSS and ranging for maximum availability, accuracy and reliability in urban and indoor environments. The statistical reliability of integrating limited GNSS with each wireless ranging systems in 1 and 2 will be evaluated. Models for reliability will be developed for autonomous driving, pedestrian navigation and contact tracing. New models for both the systematic and stochastic errors of each of the systems will be developed and the advantages and disadvantages of including each type of measurement in different user scenarios will be assessed. This work is significant because GNSS is not sufficiently available in urban and indoor environments, nor reliable for safety-of-life applications. Current Bluetooth-based ranging is also not accurate enough for reliable contact tracing. The reliability of both technologies will be improved if appropriately integrated with the three emerging wireless ranging technologies (802.11mc, 5G and UWB) targeted in this proposal. The results will be directly useful to Canadian and international automobile manufacturers, health authorities, telecommunications companies, and location-based services providers.

全球导航卫星系统（GNSS）为在户外操作的广泛用户提供定位，但室内和城市导航在可用性、准确性和可靠性方面仍然具有挑战性。大多数GNSS用户现在都是拥有无线手机的个人，他们大部分时间都在城市和室内环境中度过。支持直接距离测量的新型地面无线通信标准正在兴起，包括超宽带（UWB）、802.11mc （WiFi）和5G，而低功耗蓝牙（BLE）正在成为低精度测距和接触跟踪的标准。本研究的目的是通过将GNSS与这些测距系统相结合，将室内和城市导航提高到几厘米的水平。拟进行的研究旨在：1。将新的测距技术与城市环境中的GNSS相结合。将研究IEEE 802.11mc和5G测距的性能，并与UWB测距进行比较，并开发改进的802.11mc和5G误差模型。人工智能将用于校正测量误差，并将结果与GNSS观测相结合，以估计位置和每个系统的系统误差。2. 使用802.11mc、5G和UWB改善BLE范围。低功耗、低成本、低复杂度BLE已被确定为2020年Covid-19大流行背景下电子接触者追踪的首选方法。虽然BLE可以确定设备是否在附近，但其用于距离估计的用途有限。最近使用人工智能技术来学习识别由于障碍物导致的BLE观测变化的工作将扩展到开发一个概念，其中配备其他3种测距系统的设备子集可用于训练仅依赖BLE的低成本系统。3. 整合GNSS和测距，在城市和室内环境中实现最大的可用性、准确性和可靠性。将评估1和2中有限GNSS与每个无线测距系统集成的统计可靠性。将开发用于自动驾驶、行人导航和接触追踪的可靠性模型。将开发每个系统的系统误差和随机误差的新模型，并将评估在不同用户情景中包括每种测量的优点和缺点。这项工作意义重大，因为GNSS在城市和室内环境中的可用性不够，对于生命安全应用也不可靠。目前基于蓝牙的测距也不够精确，无法进行可靠的接触追踪。如果与本提案中针对的三种新兴无线测距技术（802.11mc、5G和UWB）适当集成，这两种技术的可靠性都将得到提高。研究结果将对加拿大和国际汽车制造商、卫生当局、电信公司和基于位置的服务提供商直接有用。