Intelligent Sub-meter Localization Based on OFDM Modulation Signal

Abstract

With the development of Internet of Thing (IoT), high accuracy positioning is of a significant importance in indoor environment. In recent years, localization technology has advanced greatly aiming at localizing the target by utilizing existing infrastructure such as LTE and Wi-Fi. However, indoor localization still faces many challenges caused by the complex indoor environment. In this chapter, first of all, a new indoor localization approach by employing the Angle-of-arrival (AOA) and Received Signal Strength (RSS) measurements in Line-of-Sight (LOS) environment is proposed. Specifically, the Channel State Information (CSI) is collected by using the commodity Wi-Fi devices with our designed three antennas to estimate the AOA of Wi-Fi signal. Second, a new direct path identification algorithm is proposed to obtain direct signal path for the sake of reducing the interference of multipath effect on AOA estimation. Third, a new objective function is constructed to solve localization problem by integrating AOA and RSS information. Although the localization problem is non-convex, the Second-order Cone Programming (SOCP) relaxation approach is used to transform it into a convex problem. Finally, the effectiveness of the proposed approach is verified based on the prototype implementation by using the commodity Wi-Fi devices. The experimental results show that this approach can achieve the median error 0.7 m in the actual LOS indoor environment. However; the localization in Non-line-of-sight (NLOS) environment is challenging since the incorrect AOA introduces large localization errors. Therefore, sensing the LOS or NLOS path between the AP and target is significantly important for improving the performance of a large number of mobile computing applications such as indoor localization. Various promising systems in current commodity Wi-Fi network rely on the frequency-dependent amplitude and phase of CSI to construct the LOS and NLOS identification scheme, but the corresponding robustness is not considered enough. With the development of Multiple-input-multiple-output (MIMO) technology with smart antennas, the spatial property of channel can be obtained effortlessly. Consequently, a potential is provided to utilize the unchanged spatial information of multipath signals to deliver robustness identification system. In this chapter, the Wi-vision, an accurate and robust LOS identification system based on indoor Single-input-multiple-output (SIMO) measurements, is proposed. The concept of Hopkins statistic is introduced to measure the distribution properties of the AOA and relative Time-of-flight (TOF) under the LOS and NLOS conditions. The experimental results show that the Wi-vision is able to achieve the LOS and NLOS detection rates above 91 and 87% respectively under different system configuration.