Indoor positioning system and method based on deep neural network

A deep neural network and indoor positioning technology, applied in neural learning methods, biological neural network models, services based on specific environments, etc., can solve the problem that the data is not comprehensive enough, and the calculation results are easily affected by indoor temperature and humidity changes, obstacles, and personnel movement Factors influence and other issues to achieve the effect of improving accuracy

Pending Publication Date: 2020-04-21
张早
View PDF0 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Traditional neural network-based fingerprint algorithm indoor positioning systems and positioning methods have certain disadvantages. For example, the collected data is not comprehensive enough, and the calculation results are easily affected by indoor temperature and humidity changes, obstacles, and personnel movement factors. This patent is based on this status. An indoor positioning system and method based on a deep neural network are provided

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Indoor positioning system and method based on deep neural network
  • Indoor positioning system and method based on deep neural network
  • Indoor positioning system and method based on deep neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041]An indoor positioning system based on a deep neural network, comprising a mobile terminal, a temperature sensor, a humidity sensor, a camera, the Internet, a router, a positioning server and a neural network chip, the mobile terminal is bidirectionally connected to the router; the temperature sensor The output is connected to the input of the router; the output of the humidity sensor is connected to the input of the router; the output of the camera is connected to the input of the router; the router is bidirectionally connected to the positioning server; the positioning server is connected to the positioning server The neural network chip is bidirectionally connected; the mobile terminal is connected to the router, the temperature sensor is connected to the router, the humidity sensor is connected to the router, and the camera is connected to the router via the Internet. The neural network chip can be built into a tablet computer, a notebook computer, or a device combinin...

Embodiment 2

[0044] An indoor positioning method based on a deep neural network, the indoor positioning method based on a deep neural network includes an offline training phase and an online positioning phase;

[0045] The offline training phase is as follows figure 2 shown, including the following steps:

[0046] Step 1, collect images, temperature and humidity data in the indoor positioning environment through the camera, temperature sensor and humidity sensor respectively, and perform image preprocessing on the image data collected by the camera;

[0047] Step 2. Input the preprocessed image data into the unsupervised convolutional neural network, extract the feature vectors and classify them, and store the classified feature vectors separately. In this block diagram, it is assumed that the unsupervised convolutional neural network finally divides the image into M group, and record the weight value of the unsupervised convolutional neural network;

[0048] Step 3: Add corresponding t...

Embodiment 3

[0060] If the user uses the positioning function continuously, compare the fingerprint vectors obtained in the online positioning stage with the fingerprint vectors of all locations obtained in the offline training stage, select several closest fingerprint vectors, and determine several corresponding positioning Location. Select the position closest to the last positioning moment among all selected positions as the current positioning position, and feed back this position to the user through the Internet.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides an indoor positioning system and method based on a deep neural network, and relates to the technical field of artificial intelligence, and the method comprises the steps: employing a neural network chip as a core, and employing a camera, a temperature sensor, a humidity sensor and a mobile terminal for needed data collection; transmitting required data through the Internet and a router; performing data preprocessing and data storage by using a positioning server; and finally, enabling a neural network chip to complete tasks such as neural network model training, optimalweight value searching, fingerprint information generation and positioning point determination. Compared with a traditional indoor positioning system and method, the indoor positioning system and method can be suitable for more complex environments, more accurate positioning can be achieved, and high practical value is achieved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to an indoor positioning system and method based on a deep neural network. Background technique [0002] With the rapid development of wireless communication networks, mobile users put forward higher requirements for location-based positioning services. In location-based services, indoor positioning is always a challenge. Due to non-line-of-sight transmission paths, multipath effects, delay distortion and other issues, the accuracy of indoor positioning has always been relatively low. In recent years, neural networks have become a hot spot in the field of artificial intelligence, and neural networks have achieved great success in fields such as image recognition, speech translation, and automatic driving. More and more researchers are trying to use neural networks to achieve breakthroughs in indoor positioning accuracy. Among them, the indoor positioning method ba...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/33H04W4/38H04W64/00G06K9/62G06N3/04G06N3/063G06N3/08
CPCH04W4/33H04W4/38H04W64/00G06N3/088G06N3/063G06N3/045G06F18/24
Inventor 张早章猛韩业王书宇
Owner 张早
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap