Target location method by optimizing the number of fingerprint elements in wireless communication environment

Abstract

The invention discloses a target positioning method by optimizing the number of fingerprint elements in a wireless communication environment, based on a short-distance wireless communication positioning system, targeting positioning accuracy and calculation efficiency, using a neural network as a research tool, combined with a training process The accuracy of the feedback and the calculation cycle continuously optimize the feature dimension; in the training stage, the training is performed according to the historical trajectory of the positioning target and the fingerprint information composed of the feature information collected under these trajectories, and the neural network is used for regression analysis, so that Position information and feature information can be mapped, and at the same time adjust the number of elements of the feature vector at the corresponding position according to the results of the test process, so as to achieve positioning with the most reasonable number of elements; finally, in the test phase, through the completion of each indoor position With the training model, the specific location of the positioning target in the room will be detected. The present invention can select the optimal number of elements of the feature vector according to the dynamic situation in the positioning process, thereby improving the efficiency of feature collection and providing more accurate position information.

Description

technical field [0001] The invention relates to an indoor positioning system based on a deep learning perspective to build a short-distance wireless communication, and belongs to the technical field of wireless communication positioning. Background technique [0002] Fingerprint recognition is an important method to solve indoor positioning problems. The so-called fingerprint (fingerprint information) refers to the characteristic information about each broadcast node collected by traditional positioning algorithms such as received signal strength (RSSI), time of arrival (TOA), time difference of arrival (TDOA) and other positioning targets. gather. Therefore, in the present invention, "fingerprints" are often used to represent the collected features, that is, the feature information from each anchor node is combined into a vector for calculation. [0003] The traditional positioning method mainly uses the collected feature information directly for calculation, because in t...

AI Technical Summary

Problems solved by technology

[0006] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a target positioning method by optimizing the number of fingerprint elements in a wireless communication environment. In the optimization of the dimensionality of the fingerprint information input into the neural network, the accuracy of the positioning is taken into account during the positioning process, and high computational efficiency can be maintained.

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