Fingerprint positioning method based on long and short time memory network model and access point selection strategy

Abstract

The invention discloses a fingerprint positioning method based on a long and short time memory network model and an access point selection strategy, and the method at least comprises the steps: S1, collecting indoor RSS signal data, and S3, completing the screening of access point data based on a preset judgment strategy. S4, carrying out localized extraction on the features of a fingerprint database by using a sliding window; and S5, training the updated fingerprint database by adopting a long-term and short-term memory network model to obtain a trained network model so as to realize indoor positioning. The access point selection strategy is adopted to reconstruct the database so as to simplify the scale of the database. Data selected and output by the AP is processed by utilizing a feature point extraction method so as to extract features with relatively large information amount; and the extracted features are input into the LSTM network model, so that the effect of enhancing the noise robustness while reducing the calculated amount is achieved, the positioning problem in an indoor complex environment is solved, and the influence on the positioning precision in a non-line-of-sight transmission background is reduced.

Description

technical field [0001] The invention belongs to the technical field of fingerprint positioning, and in particular relates to a fingerprint positioning method based on a long-short-term memory network model and an access point selection strategy. Background technique [0002] With the widespread popularization and rapid development of wireless communication network technology, people's demand for positioning services has increased dramatically. According to different scenarios of needs, positioning services can be divided into outdoor positioning and indoor positioning. Outdoor positioning can use satellite high-precision wireless navigation and positioning system, but due to the influence of indoor complex environment and non-line-of-sight transmission, this method is not suitable for indoor positioning. Therefore, how to achieve accurate positioning in indoor environment has aroused extensive interest of researchers. Indoor positioning services and location-based applicati...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to use the machine learning algorithm and the access point selection algorithm to improve the positioning accuracy, reduce the calculation amount and enhance the noise robustness, solve the positioning problem in the indoor complex environment, and reduce the impact on the positioning accuracy in the non-line-of-sight transmission background. Therefore, a fingerprint location method based on long short-term memory network model and access point selection strategy is proposed.

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