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RFID (radio frequency identification) indoor positioning method and system based on machine learning

An indoor positioning and machine learning technology, applied in the field of positioning and recognition, can solve the problems of inaccurate positioning results and low positioning accuracy, and achieve the effects of stable positioning results, high positioning accuracy, and accurate ranging.

Inactive Publication Date: 2018-11-23
FOSHAN SHUNDE SUN YAT SEN UNIV RES INST +2
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  • Summary
  • Abstract
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AI Technical Summary

Problems solved by technology

At present, RFID indoor positioning technology generally uses a positioning algorithm that has nothing to do with distance measurement for simplicity. Because it does not perform accurate distance measurement and data processing, the positioning accuracy is low, and the probability of inaccurate positioning results is greatly increased.

Method used

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  • RFID (radio frequency identification) indoor positioning method and system based on machine learning
  • RFID (radio frequency identification) indoor positioning method and system based on machine learning
  • RFID (radio frequency identification) indoor positioning method and system based on machine learning

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Embodiment Construction

[0036] refer to figure 1 , a kind of RFID indoor positioning method based on machine learning of the present invention, comprises the following steps:

[0037] Send multi-frequency carrier to the target to obtain phase difference data;

[0038] Preprocess phase difference data;

[0039] Using classification and regression algorithms to build predictive models;

[0040] The coordinates of the target are obtained according to the prediction model.

[0041] Further, the Gaussian filtering method is used to preprocess the phase difference data to filter out the abnormal phase difference data and obtain the phase difference data that meets the requirements, which specifically includes the following steps:

[0042] Establish a normal distribution model RSSI, the i-th phase difference p i The density function of is expressed as

[0043]

[0044] Among them, μ and σ are the mean and standard deviation of RSSI, respectively, m is the total amount of phase difference data; ...

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Abstract

The invention discloses an RFID (radio frequency identification) indoor positioning method and system based on machine learning. The method comprises the following steps of sending multi-frequency carrier waves to a target and acquiring phase difference data; preprocessing the phase difference data; establishing a prediction model by adopting a categorical regression algorithm; acquiring coordinates of the target according to the prediction model. Compared with a traditional technology, the method and the system have the advantages that the positioning precision is high, accurate distance measurement, data processing and modeling analysis can be carried out on the target, and therefore accurate and stable positioning results are obtained.

Description

technical field [0001] The invention relates to the field of positioning and identification, in particular to a machine learning-based RFID indoor positioning method and system. Background technique [0002] RFID is the abbreviation of Radio Frequency Identification, that is, radio frequency identification technology. RFID radio frequency identification is a non-contact automatic identification technology. It automatically identifies the target object and obtains relevant data through radio frequency signals. The identification work does not require manual intervention and can work in various harsh environments; RFID technology can identify high-speed moving objects and can Simultaneously identify multiple tags, quick and easy to operate. At present, RFID indoor positioning technology generally uses a positioning algorithm that has nothing to do with distance measurement for simplicity. Since it does not perform accurate distance measurement and data processing, the positio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20
CPCG01C21/206
Inventor 谭洪舟张浩曾衍瀚廖裕兴陈曦恒王嘉奇方巍陈翔张鑫
Owner FOSHAN SHUNDE SUN YAT SEN UNIV RES INST
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