Indoor positioning method based on ridge regression and extreme learning machine

An extreme learning machine and indoor positioning technology, applied in the field of machine learning, can solve problems such as time-consuming, cumbersome, and unsatisfactory use effects, and achieve the effects of low sensitivity, good robustness, and guaranteed accuracy

Inactive Publication Date: 2019-01-18
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, in this method, the positive value K is required to be specified by the user, and the process of selecting the optimal positive value K is very cumbersome and time-consuming, so this method is not ideal in actual use.

Method used

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  • Indoor positioning method based on ridge regression and extreme learning machine
  • Indoor positioning method based on ridge regression and extreme learning machine
  • Indoor positioning method based on ridge regression and extreme learning machine

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Embodiment Construction

[0045] Such as figure 1As shown, the present invention discloses an indoor positioning method based on ridge regression and extreme learning machine.

[0046] In order to better illustrate the technical solution of the present invention, the extreme learning machine (ELM) technology in the prior art will be specifically explained next.

[0047] The ELM algorithm is a machine learning algorithm for single-layer feed-forward neural networks. It randomly generates input weights and thresholds between the input and hidden layers. use Calculate the weight β between the hidden layer and the output layer, is the generalized inverse of the randomly generated hidden layer output matrix H.

[0048] When the number of training data N is greater than the number of hidden layer nodes L But H T H is sometimes singular, in H T When H is singular,

[0049] In the offline stage of the algorithm, the calculation of K includes the following steps. First, it is necessary to calculate...

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Abstract

The invention reveals an indoor positioning method based on ridge regression and an extreme learning machine, and the method comprises the following steps: S1, an offline data set construction step: collecting wireless signal receiving intensity data in a positioning area, and establishing an offline data training set; S2, an offline learning step: learning the relation between the wireless signalreception intensity and the target position in the offline data training set through using the ridge regression technique and the extreme learning machine technology, and performing training to obtain a position-based recursive model; S3, an online data acquisition step: performing the online collection of the wireless signal receiving intensity data at a to-be-estimated position and substitutingthe wireless signal receiving intensity data into the position-based recursive model to obtain a position estimation result. The method has the advantages of good learning stability at the offline phase and high positioning accuracy at the online phase, and can fully meet the actual use requirements. Meanwhile, the method has low sensitivity to abnormal data elements in the offline training dataset and excellent anti-interference performance.

Description

technical field [0001] The invention relates to a positioning method, in particular to an indoor positioning method based on ridge regression and extreme learning machine, belonging to the field of machine learning. Background technique [0002] With the continuous development of communication and intelligent industries, positioning technology plays an increasingly important role in our daily life. Although GPS can provide high-precision positioning results outdoors, it is not effective in complex indoor environments. Therefore, how to achieve precise positioning in the indoor environment has become a hot topic in current research. [0003] At present, there are many indoor positioning systems on the market, mainly including RFID-based positioning systems, infrared-based positioning systems, ultrasonic-based positioning systems, and Bluetooth-based positioning systems. Though above-mentioned system can satisfy people's use demand to a certain extent. However, when these s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/02H04W4/33H04W64/00G06N3/04G06N3/08
CPCH04W4/02H04W4/33H04W64/00G06N3/08G06N3/048
Inventor 颜俊冯志跃钱琛曹艳华
Owner NANJING UNIV OF POSTS & TELECOMM
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