RSSI indoor positioning and distance measuring method based on neural network learning and an indoor positioning platform

Abstract

The invention belongs to the technical field of wireless communication, and discloses an RSSI indoor positioning and distance measuring method based on neural network learning, and an indoor positioning platform, wherein communication is established between a target node and an anchor node, and collected data is stored in a set RSSI[i] = {RSSIi1, RSSIi2, ..., RSSIiN}; setting a screening probability p, determining an upper limit value RSSImax and a lower limit value RSSImin according to a Gaussian model of the RSSI; storing the RSSI in a set RSSI[i] in a range of [RSSImin, RSSImax] into a setRSSI_gauss[i]; averaging RSSI values in the set RSSI_gauss[i]; training a strong separator by combining an algorithm of a particle swarm optimization neural network and an idea of an iterator; converting the RSSI into a distance between the anchor node and the target node by using the strong separator; and obtaining a solution of the target node by using a maximum likelihood estimation method. According to the method, the workload is reduced, the larger error existing in a single algorithm is reduced, and the accuracy of converting the RSSI value into the distance is improved, so that the positioning accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to an RSSI indoor positioning and ranging method and an indoor positioning platform based on neural network learning. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: In recent years, indoor positioning as a location-based service has become a hot research topic. RSSI-based indoor positioning technology is widely used in the field of wireless communication technology because of its simplicity, low cost and low hardware requirements. Its main idea is to obtain signal strength information through mutual communication between the anchor node and the target node, and convert the filtered signal strength information into the distance between the target node and the anchor node. When the collected distance information exceeds a certain amount, it can be The coordinate position of the target node i...

AI Technical Summary

Problems solved by technology

[0004] To sum up, the problems existing in the existing technology are: when converting the signal strength value to the distance, the conversion accuracy is low, the calculation amount is large, the operation is complicated, and the positioning result is inaccurate

[0006] Due to the complex indoor environment, many obstacles, and people walking around, it is more difficult to convert the RSSI value into a distance value. If the method of calculating environmental parameters is applied, the calculated environmental parameter values ​​​​cannot represent the entire indoor environment, and cannot be followed. Real-time switching of environmental parameters when there are people and no one is there, resulting in a large distance conversion error

If the fingerprint method is applied (the method that does not use the measurement of environmental parameters), the workload is huge, and if the fingerprint method is established in an unmanned environment, it is not suitable for the situation where people walk around, and vice versa.

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