Indoor positioning method based on manifold learning and improved support vector machine

Abstract

The invention discloses an indoor positioning method based on manifold learning and an improved support vector machine. The method comprises a step of determining a positioning area, dividing the positioning area according to an indoor structural characteristic and a layout characteristic, and obtaining a classification result, a step of obtaining offline training data, and collecting hotspot RSS signal values which can be received by the reference points in different classification area as a training data set, a step of using an isometric mapping algorithm to carry out training data characteristic extraction, a step of using the training data to carry out support vector machine classified training, using a taboo search algorithm to carry out support vector machine classification hyper parameter searching, and establishing the support vector regression model of each category at the same time, a step of carrying out online positioning, collecting the RSS signal value of each hotspot of a target, using a support vector machine classification model to carry out classification, and obtaining the rough positioning area of the target, and a step of carrying out the accurate positioning of the target by using the support vector regression model according to the classification result. According to the method, the time-varying characteristic of the wireless signal intensity is effectively suppressed, and the precision is obviously improved.

Description

technical field [0001] The invention relates to the field of indoor positioning, in particular to an indoor positioning method based on manifold learning and improved support vector machines. Background technique [0002] In the indoor precise positioning problem, scholars at home and abroad have carried out relevant research, and have continuously proposed many effective and feasible positioning algorithms. Among them, due to the advantages of wireless local area network (WLAN) that can achieve full coverage and low cost, the indoor positioning algorithm based on WLAN scene analysis has become the main research direction of universities and research institutions at home and abroad. This algorithm is called fingerprint positioning, and its essence is to establish a one-to-one mapping relationship between RSS (signal strength) and specific locations, so as to perform positioning. The difficulty of this method lies in the establishment of the mapping relational database (fing...

AI Technical Summary

Problems solved by technology

In the process of SVM establishment, the selection of its hyperparameters directly affects the calculation results. The traditional grid search algorithm has low precision and long search time, which affects the real-time performance of indoor positioning. Therefore, the selection method of SVM hyperparameters is also particularly important.

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