Machine learning-based hybrid kernel function indoor positioning method

Abstract

A machine learning-based hybrid kernel function indoor positioning method disclosed by the present invention comprises the steps of firstly establishing a fingerprint map library, and taking the fingerprint map library as a training data set; then utilizing a weighted summation method to construct a hybrid kernel function, and using a support vector regression algorithm and a v-folding cross validation method in the machine learning algorithms to train to obtain an optimal weight coefficient and an optimal kernel parameter of the hybrid kernel function; and finally under the premises of the optimal weight coefficient and the optimal kernel parameter, carrying out the offline training learning on the training data set to obtain the fitting functions of an x coordinate and a y coordinate separately, and then utilizing the fitting functions to carry out the online learning on an RSSI value received by a target, thereby obtaining the position coordinate of the target. Compared with the conventional indoor positioning algorithms of a BP neural network algorithm, a k-nearest neighbor algorithm, a linear kernel function algorithm, a polynomial kernel function algorithm and a Gaussian kernel function algorithm, the positioning precision of the algorithm of the present invention is higher.

Description

Technical field [0001] The invention relates to wireless communication technology and sensor technology, in particular to a hybrid kernel function indoor positioning method based on machine learning, which belongs to the technical field of communication positioning. Background technique [0002] With the development of wireless sensor network technology in recent years, location services are widely used in personal and commercial applications. Traditional positioning algorithms include received signal strength indication (RSSI) based algorithms, based on time of arrival (TOA), based on time difference of arrival (TDOA), and based on angle of arrival (AOA) algorithms[1][2][3]. Among them, the RSSI-based algorithm is widely respected for its low power consumption and low cost. [0003] Based on RSSI algorithm, it is divided into two categories: ranging and positioning algorithm [4], fingerprint positioning algorithm [5] [6]. After the ranging positioning algorithm converts the RSSI...

AI Technical Summary

Problems solved by technology

[0006] The current support vector machine algorithm mainly uses a single kernel function in the selection of kernel functions, but the generalization ability and robustness of a single kernel function have limitations, and it is not applicable to all specific problems, and when the sample features contain heterogeneous When the information and sample size are huge, the disadvantage of a single kernel function becomes more apparent.

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