Abstract
With the increasing demand of location-based services, the indoor ranging method based on Wi-Fi has become an important technique due to its high accuracy and low hardware requirements. The complicated indoor environment makes it difficult for wireless indoor ranging systems to obtain accurate distance measurements. This paper presents an Extended Kalman filter-based approach for indoor ranging by utilizing transmission channel quality metrics, including Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The proposed ranging algorithm scheme is implemented and validated with experiments in two typical indoor environments. A real indoor experiment demonstrates that the ranging estimation accuracy of our algorithms can be significantly enhanced compared with the typical algorithms. The ranging estimation accuracy is defined as the cumulative distribution function of the distance error.
Highlights
For positioning in outdoor environments, the Global Position System (GPS) [1,2] can provide very accurate positioning results
Based on the reasons above, we propose the indoor ranging algorithm based on Received Signal Strength Indicator (RSSI) and Channel State Information (CSI)
This paper demonstrates the feasibility of this method in a high-load Access Point (AP) environment
Summary
For positioning in outdoor environments, the Global Position System (GPS) [1,2] can provide very accurate positioning results. According to the position estimation method in wireless sensor networks, the ranging algorithm is divided into two categories: ranging-based algorithm and range-free algorithm The former method includes Angle of Arrival(AOA) [4], Time of Arriva (TOA) [5], Time Difference of Arrival (TDOA) [6], Received Signal Strength (RSS) [7], Channel State Information (CSI) [8] etc., and calculate or estimate the distance between the node and the reference. Most of the current algorithms are range-free-based using RSI and CSI These positioning methods are currently based on the similarity of the indoor environment during the positioning and training phases. The proposed algorithm breaks the limitations of traditional RSSI or CSI-based fingerprint positioning and can be applied to online positioning in any indoor environment.
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