Abstract
Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.
Highlights
With the continuous development and popularization of wireless communication technology, Wi-Fi has become more and more widely used in daily life, and there is a growing demand for the location-based service (LBS) [1]
When collecting in the offline phase build the stage, Channel State Information (CSI) data collection should be carried out first, and the collected data should be preprocessed in the same way as in the offline stage, and the processed data should be compared with the data in the fingerprint database to obtain the positioning result.inInthe order to reduce thesurrounding interference fingerprint database and when conducting positioning matching online phase, the caused by changes in indoor environment to CSI signals, tried tounchanged
This paper proposes a passive indoor positioning system based on CSI
Summary
With the continuous development and popularization of wireless communication technology, Wi-Fi has become more and more widely used in daily life, and there is a growing demand for the location-based service (LBS) [1]. Due to the large difference between the physical model and the actual transmission environment of signals, and the existence of some interference factors that cannot be estimated by the physical model, the positioning effect is often not as high as that of the fingerprint-based positioning method. Since a large amount of state information in the real environment is collected, the fingerprint location method can reduce the impact of environmental changes on the positioning results to some extent, resulting in a higher accuracy of positioning results. In BiLoc [36], the authors developed a deep learning-based algorithm to exploit bimodal data Both the estimated angle of arriving (AOA) and average amplitudes of two adjacent antennas are used as location features for building the fingerprint database.
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