Abstract
With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.
Highlights
Location-based applications and services are concerned with people’s daily lives, and have attracted increasing attention
We considered the multipath effect and time-variance of Channel state information (CSI) signals in indoor environments
We proposed FapFi, a passive indoor localization system leveraging the CSI amplitude and phase features to yield the fingerprint
Summary
Location-based applications and services are concerned with people’s daily lives, and have attracted increasing attention. FIFS uses the diversity of the original CSI data in the time and frequency domains It utilizes a weighted average CSI value based on multiple antennas to improve the accuracy of indoor positioning. (3) How to automatically cluster the different locations in large-scale fingerprint datasets and in short response times especially with high-accuracy requirements These studies did not fully apply the fine-grained CSI amplitude and phase information, which made it impossible to achieve more accurate positioning. To improve the indoor positioning accuracy and the overall effect, this paper presents FapFi, a passive indoor fingerprint system based on WiFi using the fusion amplitude and phase information of the CSI signal. We proposed to use a fine-grained physic layer information CSI for indoor localization and processed the CSI amplitude and phase data to obtain stable and robust fingerprint features while reducing the signal interference from environmental factors.
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