Weak multipath effect identification for indoor distance estimation

Abstract

Wireless LANs, especially WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. Indoor localization is an essential modules of these applications. A primary concern in designing scenario-tailored application is to obtain precise estimated distance combatting with harsh indoor wireless signal propagation issues, particularly multipath effect. The conventional propagation model based on received signal strength indicator(RSSI) is easily affected by temporal and spatial fluctuation due to the multipath effect, which leads to most of the distance estimation errors in current indoor localization systems. Intuitively, these positions in weak multipath effect(WME) conditions, which are slightly affected by multipath effect, perform better under free space propagation model. Therefore, the ability to distinguish weak multipath effect, which is slightly affected by multipath effect is a key enabler for accurate distance estimation. Enabling such capabilities on commercial WiFi infrastructure, however, is difficult due to the coarse multipath resolution with the MAC layer RSSI. In this paper, we propose a universal precise distance estimation scheme based on weak multipath effect identification, leveraging the channel state information(CSI) from the PHY layer. In our distance estimation system, we first select positions which are identified as weak multipath effect conditions. Then we build a free space propagation model with RSSI to estimate distance between the transmitter and the receiver, choosing these selected positions. Experimental results demonstrate that choosing positions in weak multipath effect conditions can effectively improve the accuracy of distance estimation in a variety of typical indoor environments.