基于无线电通信的室内三边定位技术的比较

Dominik Csík, Peter Sarcevic, Richard Pesti, Á. Odry
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摘要

随着物联网(IoT)的普及,各种基于无线电通信的技术受到越来越多的关注,其在无GPS信号的室内定位问题中的应用变得越来越有意义。研究了基于三边定位算法的室内定位位置估计方法;这种方法使用接收信号强度指示(RSSI)值与自由空间路径损耗(FSPL)模型。综合实验室测量采用了不同的基于无线电通信的技术,即433 MHz RSSI、2.4 GHz WiFi RSSI、超宽带(UWB) RSSI和超宽带飞行时间(TOF)。然后,利用粒子群优化算法(PSO)进行数值优化,确定各锚点和各工艺的FSPL模型参数。该方法可以利用三边测量法计算测量节点的位置。结果表明,433 MHz RSSI和UWB TOF技术优于其他两种技术。采用超宽带TOF技术,实现了165.97 cm的精度。433 MHz RSSI技术提供了166.60 cm的第二佳解决方案。WiFi RSSI的精度为227.89 cm,而UWB RSSI的精度最差,为252.08 cm。该研究通过实验验证了UWB TOF和433 MHz RSSI提供了最合适的测量,这使得这些技术能够在传感器融合算法中实现。
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Comparison of different radio communication-based technologies for indoor localization using trilateration
With the spread of Internet of Things (IoT) different radio communication-based technologies are gaining more attention, and their application in indoor localization problems where GPS signal is not available, becomes more and more relevant. This paper addresses the trilateration algorithm-based position estimation approach in indoor localization; this approach uses Received Signal Strength Indication (RSSI) value with the Free Space Path Loss (FSPL) model. Comprehensive laboratory measurements were performed with different radio communication-based technologies namely 433 MHz RSSI, 2.4 GHz WiFi RSSI, ultra-wideband (UWB) RSSI, and UWB Time of Flight (TOF). Then, numerical optimization using the Particle Swarm optimization (PSO) algorithm to determine the parameters of the FSPL model for each anchor and each technology. This method enabled the utilization of trilateration to calculate the position of the measurement node. The obtained results show that the 433 MHz RSSI and the UWB TOF outperform the other two technologies. Using UWB TOF technology, the achieved accuracy was 165.97 cm. 433 MHz RSSI technology provided the second-best solution with 166.60 cm. WiFi RSSI provided 227.89 cm accuracy, while the worst case was obtained by UWB RSSI with 252.08 cm. The study experimentally validates that the most appropriate measurements are provided by UWB TOF and 433 MHz RSSI, which enables the implementation of these technologies in sensor fusion algorithms.
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