RSS and CSI combined fingerprint indoor positioning method based on deep learning

An indoor positioning and deep learning technology, applied in the field of wireless communication and indoor positioning, can solve the problems of redundant information and noise, difficult positioning accuracy, low RSS fingerprint positioning accuracy and stability, etc., to reduce computational complexity and consumption time, rich fingerprint information, and high positioning accuracy

Inactive Publication Date: 2019-10-25
XIDIAN UNIV
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Problems solved by technology

The essence of DeepFi is to train a stacked restricted Boltzmann machine based on each reference point. When the positioning environment is larger and there are more reference points, the computational complexity and training time of the DeepFi algorithm will increase significantly.
On the other hand, the main idea of ​​the DeepFi localization method is to consider the abstraction of CSI at a single reference point, while at the same time, the connection between different reference points is ignored.
[0005] To sum up, the problems existing in the existi...

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  • RSS and CSI combined fingerprint indoor positioning method based on deep learning
  • RSS and CSI combined fingerprint indoor positioning method based on deep learning
  • RSS and CSI combined fingerprint indoor positioning method based on deep learning

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[0038] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0039] Due to the coarse-grained and unstable characteristics of RSS, the positioning accuracy and stability based on RSS fingerprints are not high, and there are redundant information and noise problems that pose challenges to positioning accuracy. The present invention firstly considers combining RSS and CSI information to enrich fingerprint features, mining fingerprint features based on deep learning and realizing a high-precision indoor positioning method.

[0040] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0041] Such as figure 1...

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Abstract

The invention belongs to the technical field of wireless communication and indoor positioning. The invention discloses an RSS and CSI combined fingerprint indoor positioning method based on deep learning. The method comprises the following steps: collecting and processing RSS and CSI information in an offline stage, training a CSI processing model based on a deep auto-encoder, encoding CSI at a reference point, constructing a fingerprint library by combining RSS and CSI encoding, and dividing sub-fingerprint libraries according to an AP selection algorithm; training a positioning prediction model based on a deep neural network for each sub-fingerprint library; in the online positioning stage, according to the CSI processing model trained in the offline stage and based on the deep auto-encoder, carrying out CSI coding on the to-be-measured point, calculating joint RSS and CSI fingerprints, and carrying out prediction positioning based on a deep neural network model. According to the method, RSS and CSI information are combined, fingerprint information is enriched, and the calculation complexity and time consumption of a positioning prediction model are reduced; and the positioning precision is high, the expandability is strong, and the mobility is high.

Description

technical field [0001] The invention belongs to the technical field of wireless communication and indoor positioning, and in particular relates to a fingerprint indoor positioning method based on deep learning combined with RSS and CSI. Background technique [0002] At present, the closest existing technology: with the popularization of wireless networks and mobile devices, WLAN-based indoor positioning has received more and more attention. Its positioning accuracy can reach meter level, no need to deploy additional equipment, and the positioning cost is low. It is easy to promote in the application of daily life. WLAN-based indoor positioning technology can be divided into two types: ranging-based positioning algorithm (Ranging-based Localization) and location fingerprint-based indoor algorithm (Fingerprint-based Localization). Due to the complex and changeable indoor environment, the wireless signal is prone to reflection, refraction and scattering due to factors such as ...

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Application Information

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IPC IPC(8): H04W4/021H04W4/33G06N3/08G01S5/02
CPCG01S5/0257G06N3/08H04W4/021H04W4/33
Inventor 刘俊宇厚丹妮盛敏李建东彭琳琳郑阳
Owner XIDIAN UNIV
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