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Transfer learning indoor positioning method based on improved TrAdaBoost

A transfer learning and indoor positioning technology, applied in the field of wireless communication, can solve the problems of high algorithm complexity and time-consuming

Active Publication Date: 2021-05-28
HEFEI UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0006] To sum up, although the collection and update of fingerprint points based on group intelligent perception has achieved good results, it depends on the movement of smart mobile terminals and ordinary users, which is a time-consuming task.
The migration learning method based on features, models and kernel learning has also achieved good positioning results in various scenarios, but the algorithm complexity is high

Method used

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  • Transfer learning indoor positioning method based on improved TrAdaBoost
  • Transfer learning indoor positioning method based on improved TrAdaBoost
  • Transfer learning indoor positioning method based on improved TrAdaBoost

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Embodiment Construction

[0065] In this example, figure 1 It is a flow chart for the realization of the indoor positioning system based on the improved TrAdaBoost transfer learning, such as figure 2 As shown, a transfer learning indoor positioning method based on the improved TrAdaBoost is carried out as follows:

[0066] Step 1. Select a rectangular positioning area in the indoor space as the source domain, divide the rectangular positioning area into n rectangular blocks evenly, take the center point of each rectangular block as the fingerprint point of the corresponding rectangular block, when the rectangular positioning area When the scene changes, take the changed rectangular positioning area as the target domain; set 20 points in the positioning area as fingerprint points, and set 8 points in each positioning area as test points;

[0067] Step 2. Use the router as the sending device of the WIFI signal in the rectangular positioning area, which is recorded as AP, and use the network card as the...

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Abstract

The invention discloses a transfer learning indoor positioning method based on improved TrAdaBoost, and the method comprises the steps: 1, enabling an original scene of a collected fingerprint database to serve as a source domain, and defining a new scene or a scene with a changed internal environment as a target domain; 2, coding the CSI amplitude data after the linear transformation is eliminated by using a One-Hot algorithm; 3, performing cross matching on the processed amplitude data by using a One-vs-Rest algorithm; 4, using a TrAdaBoost algorithm to adjust weights of data of a source domain and a target domain, training a final multi-classifier, and combining fingerprint features of two scenes to construct a new fingerprint library for positioning the target domain; and 5, finally, estimating the position of the test point through confidence regression. According to the invention, the fingerprint database with changed scenes can be updated or the fingerprint database with new scenes can be established at a low cost, and the algorithm complexity is reduced on the premise of ensuring high positioning precision.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and specifically relates to an indoor positioning method based on improved TrAdaBoost transfer learning. Background technique [0002] At present, due to the extensive installation of network infrastructure, WLAN-based wireless terminal equipment has been deployed more and more in various public places such as shopping malls, offices, airports, and railway stations. Wi-Fi-based wireless positioning technology has the advantages of low deployment cost and open access, and has become one of the most promising positioning methods in the field of indoor positioning. [0003] Signal strength (RSSI) is widely used in WiFi-based indoor positioning, which is the aggregated signal strength of multiple signal paths, because of its simplicity and low hardware requirements, many existing indoor positioning systems use RSSI values ​​as fingerprints. But it is only a rough representation of the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W16/20H04W4/02H04W64/00
CPCH04W16/20H04W4/025H04W64/006
Inventor 王昱洁张勇何飞吴承斌
Owner HEFEI UNIV OF TECH
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