Indoor localization method based on clustering algorithm analytical data optimization

Abstract

The invention relates to an indoor localization method based on clustering algorithm analytical data optimization, which is used for carrying out accurate localization on an unknown node in a complex index environment. The indoor localization method comprises the steps of: executing an RSSI (Received Signal Strength Indicator) signal processing optimization strategy, i.e., optimizing an RSSI value by using a clustering analysis Gaussian hybrid filtering model so as to eliminate the problems of dispersed intersection and serious jitter of the RSSI value, which exist due to factors of a multipath effect, a barrier and the like, and obtain one more reliable and reasonable RSSI value; adopting a fitting RSSI distance measurement model, i.e., according to an RSSI-distance conversion curve, carrying out fitting of the curve by adopting a least square method so as to obtain a logarithm path loss model suitable for a current environment; and then estimating out position information of the unknown node by using a weighted centroid localization algorithm. According to the indoor localization method disclosed by the invention, by an optimal RSSI distance measurement algorithm, accuracy of localization and distance measurement is improved, so that adaptability and localization accuracy of the localization algorithm are improved; and the indoor localization method is suitable to apply and popularize in the complex indoor environment.

Description

Technical field [0001] The invention relates to an indoor positioning method. In particular, it relates to an indoor positioning method based on clustering algorithm analysis data optimization that optimizes RSSI measurement values ​​and improves logarithmic path loss model positioning in a complex indoor environment. Background technique [0002] With the maturity of IEEE802.11 technology and the popularization of mobile devices and wireless local area networks around the world, the positioning of mobile users has become a research hotspot. As the most widely used outdoor positioning technology in the world, GPS has been applied in many fields. However, its signals are easily interfered and blocked by obstacles. In dense urban areas, tunnels, indoors and other environments, positioning errors are compared The accuracy is difficult to meet actual requirements. Therefore, mobile terminal-based positioning technology applied in indoor and urban dense areas has an urgent demand an...

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Problems solved by technology

Second, the RSSI-distance logarithmic transformation model is greatly affected by environmental factors and noise such as multipath attenuation. There is a large error between the distance information obtained by the logarithmic curve model and the real distance, resulting in positioning accuracy that cannot meet indoor positioning. needs

[0006] For example, due to the interference of noise such as multipath attenuation and obstacles, the signal strength value often fluctuates greatly, which makes the distance value converted from the RSSI value have a large error, and the RSSI-distance conversion model is greatly affected by the environment and other factors. The distance from the unknown node to the beacon node based on the RSSI ranging method is far from the actual distance

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