Optimized indoor positioning method

Abstract

The invention provides an optimized indoor positioning method comprising a centroid positioning algorithm module, an optimal beacon node selection module, a to-be-positioned area division module, anda continuous positioning module. The method comprises the following steps: firstly using the Dick Xun anomaly detection method to select an optimal beacon node on the surrounding, and using the Gaussian filtering method to filter the beacon; then using the centroid positioning method to determine the initial area of the target node, dividing a primary target location area, shrinking the area of the target node, and determining a final location of the target node in a discrete positioning stage; and finally, using the particle filter algorithm to estimate the location of the target node at thenext moment based on the final location.

Description

technical field [0001] The invention belongs to the technical field of wireless communication and embedded systems, and in particular relates to a positioning optimization method combining discrete positioning and continuous positioning based on low-power bluetooth. Background technique [0002] In recent years, with the maturity of the key technologies of the Internet of Things and the development of intelligent navigation technology, location-based services have received widespread attention. Especially in indoor places such as large shopping malls, museums, and underground parking lots, users often need to know their location information. The extensive needs of users have greatly promoted the development of wireless sensor network positioning technology. The indoor positioning technology is an important part and one of the typical application forms of the wireless sensor network positioning technology. [0003] The indoor environment is relatively complex, and currently...

AI Technical Summary

Problems solved by technology

[0003] The indoor environment is relatively complex. Currently the most mature and commonly used GPS positioning technology cannot perform high-precision positioning operations indoors because it is easily blocked by obstacles such as building walls.

At the same time, there are still many deficiencies in the existing indoor positioning algorithms, resulting in the low accuracy of indoor ranging and positioning based on Bluetooth RSSI.

[0004] At present, the commonly used centroid positioning algorithm and trilateral positioning algorithm have poor positioning accuracy, low real-time performance, and cannot continuously track and locate moving targets. However, in actual situations, the targets are not stationary. Discrete positioning and trilateral positioning will inevitably lead to problems such as low positioning accuracy, error instability, etc.

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