As positioning solutions integrate multiple components to improve accuracy, the number of parameters that require calibration has increased. This paper studies a calibration-free positioning technique using Wi-Fi ranging and pedestrian dead reckoning (PDR), where every parameter in the system is optimized in real-time. This significantly decreases the time and effort required to perform manual calibration procedures and enables the positioning solution to achieve robust performance in various situations. Additionally, this paper studies an efficient way of performing irregular Wi-Fi ranging procedures to improve battery life and network performance of mobile devices. The positioning performance of the proposed method was verified using a real-time Android application on several mobile devices under a large indoor office environment. Without any calibration, the proposed method achieved up to 1.38 m average positioning accuracy for received signal strength (RSS)-based ranging scenarios, which differs only by 30 cm from the benchmark assuming perfect calibration. In addition, the proposed method achieved up to 1.04 m accuracy for round trip time (RTT)-based ranging scenarios with a 40 MHz bandwidth configuration, which differs only by 10 cm from the benchmark.
Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentation
CNIT/WiLab - National Wireless Communication Laboratory（CNIT/WiLab-国家无线通信实验室）；Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome（信息工程、电子与电信系，萨皮恩扎罗马大学）
Performance Evaluation of Mobile Base Station under different Network Sizes on Cluster-Based Wireless Sensor Networks
无线传感器网络中基站的位置对网络的生存时间有着重要的影响。提出了一种基于簇的无线传感器网络移动基站定位算法，该算法综合考虑了簇首节点在网络中的位置和剩余能量，并对不同网络规模（100 m × 100 m、200 m × 200 m和300 m × 300 m）下的定位性能进行了评估。使用OMNeT++进行仿真，并与两个不同的静态BS位置，包括中心和外部，HEED协议的方法进行了比较。结果表明，在所有网络规模下，移动基站的性能都优于中心基站和外部基站。此外，所提出的方法和其他方法之间的性能差异随着网络规模的增加而增加，这表明所提出的移动BS定位也提供了可扩展性。
