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A WLAN fingerprint positioning method based on deep learning

A technology of fingerprint positioning and deep learning, applied in neural learning methods, services based on location information, services based on specific environments, etc., can solve the problems of reducing WLAN fingerprint positioning performance, achieve high positioning accuracy, good positioning performance, and improve The effect of positioning accuracy

Active Publication Date: 2021-12-07
NANJING TECH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, indoor radio propagation is time-varying and easily affected by multipath effects, shadowing effects, or dynamic environments, thereby degrading the performance of WLAN fingerprinting, not to mention mobile users with only a small amount of available RSS data.
Therefore, high-precision positioning of indoor mobile users based on WLAN fingerprints has become a serious challenge.

Method used

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  • A WLAN fingerprint positioning method based on deep learning
  • A WLAN fingerprint positioning method based on deep learning
  • A WLAN fingerprint positioning method based on deep learning

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Embodiment Construction

[0031] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0032] The present invention designs a WLAN fingerprint positioning method based on deep learning, such as figure 1 As shown, the steps are as follows:

[0033] Step 1: In the offline stage, establish a position coordinate system in the indoor area to be positioned, select a number of reference points in the area and a training track composed of multiple points that simulate the user's movement, and record the reference points and track points. Position coordinates.

[0034] Step 2: Use the terminal device to measure the received signal strength from multiple access points at each reference point and each point of the trajectory, and then use the received signal strength vector measured at each reference point and each point of the trajectory and the corresponding position coordinates respectively Establish fingerprint database 1 and fing...

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Abstract

The invention discloses a WLAN fingerprint positioning method based on deep learning. In the offline stage, the fingerprint database is first established by using the received signal strength measured at each reference point and each point of the track, and then the sparse autoencoder is improved by using the fingerprint database to train and stack. , and finally use the fingerprint database and the stacked improved sparse autoencoder to build a recurrent neural network; in the online stage, use the stacked improved sparse autoencoder and the recurrent neural network trained in the offline stage to realize mobile user positioning. The invention integrates the fingerprint algorithm of the stacked improved sparse autoencoder and the tracking algorithm of the cyclic neural network, and has high positioning accuracy.

Description

technical field [0001] The invention belongs to the technical field of indoor positioning, and in particular relates to a WLAN fingerprint positioning method. Background technique [0002] With the development of wireless communication and mobile computing, people's demand for location based service (location based service, LBS) grows rapidly. Although satellite positioning and navigation systems, such as global positioning system (global positioning system, GPS) and BeiDou navigation satellite system (BeiDou navigation satellite system, BDS) can meet most outdoor LBS requirements, the signal attenuation caused by the obstruction of buildings makes these outdoor Positioning and navigation systems are not suitable for indoor scenarios. At the same time, people spend most of their time indoors every day. Therefore, indoor localization has been extensively studied in the past few years for its applications and commercial potential. [0003] So far, many indoor positioning me...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W4/02H04W4/029H04W4/33H04W4/021H04W64/00G06N3/04G06N3/08
CPCH04W4/023H04W4/029H04W4/33H04W4/021H04W64/006G06N3/088G06N3/045
Inventor 孙永亮柏君航张权
Owner NANJING TECH UNIV