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WiFi (Wireless Fidelity) indoor locating system based on deep learning

An indoor positioning and deep learning technology, applied in positioning, radio wave measurement systems, measuring devices, etc., can solve problems such as signal volatility and positioning technology that cannot achieve positioning accuracy, so as to save manpower, meet performance requirements, and meet high-precision positioning Effect

Active Publication Date: 2017-12-29
SHANGHAI JIAO TONG UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0007] Aiming at the deficiencies of the existing technology, the present invention proposes a WiFi indoor positioning system based on deep learning to solve the problems caused by multipath effect, signal fading and other noise interference when the indoor received signal strength signal is in the space-time threshold. For the problem of signal volatility, explore the environmental attributes inside the signal through the deep belief network, extract the characteristic fingerprints for the final target positioning, and effectively achieve the positioning accuracy that cannot be achieved by the current positioning technology

Method used

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  • WiFi (Wireless Fidelity) indoor locating system based on deep learning
  • WiFi (Wireless Fidelity) indoor locating system based on deep learning
  • WiFi (Wireless Fidelity) indoor locating system based on deep learning

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Embodiment Construction

[0034] To further illustrate the various embodiments, the present invention is provided with accompanying drawings. These drawings are a part of the disclosure of the present invention, which are mainly used to illustrate the embodiments, and can be combined with related descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, those skilled in the art should understand other possible implementations and advantages of the present invention. Components in the figures are not drawn to scale, and similar component symbols are generally used to denote similar components.

[0035] The present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0036] refer to figure 2 As shown, the schematic diagram of the structure established for the coarse fingerprint library. The offline data acquisition module 100 includes a wireless sensor module 101 and an offline r...

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Abstract

The invention belongs to the technical field of indoor locating, and particularly relates to a WiFi (Wireless Fidelity) indoor locating system based on deep learning. The WiFi indoor locating system comprises an offline data acquiring module (100), a rough fingerprint library building module (200), a feature fingerprint library extracting module (300), an online data fusion module (400) and a target position outputting module (500) which are connected in sequence. Through adoption of the system, the problem of signal fluctuation of an indoor received signal strength signal caused by a multipath effect, signal attenuation and other noise interferences in a time-space domain is solved; the internal environment property of the signal is explored through a deep belief network; final target locating is performed by extracting a feature fingerprint; and a locating accuracy which is unavailable in the prior art is effectively achieved.

Description

technical field [0001] The invention belongs to the technical field of indoor positioning, and more specifically, relates to a WiFi indoor positioning system based on deep learning. Background technique [0002] As location-based services are more and more widely used in indoor environments, the research on indoor positioning technology has attracted more and more scientific research and commercial workers. The indoor positioning technology based on WiFi fingerprint has become one of the most popular indoor positioning technologies and has been put into application in various indoor places. However, the inherent volatility of the wireless signal itself causes a large positioning error in the positioning stage. In addition, in a complex indoor environment, due to multipath effects, signal fading and other noise effects, the received signal strength varies complexly in the spatio-temporal threshold. [0003] In order to solve the above technical problems, many technicians ha...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W64/00H04W84/12G01C21/20G01S5/02G01S5/10
CPCG01C21/206G01S5/0252G01S5/10H04W64/00H04W84/12
Inventor 钱久超洪燕刘佩林
Owner SHANGHAI JIAO TONG UNIV
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