High-robustness and high-precision triangle centroid location method

Abstract

The invention relates to a high-robustness and high-precision triangle centroid location method. The method comprises the following steps of searching three WiFi base stations used for location to obtain RSSI (Received Signal Strength Indicator) values of the three base stations; smoothly collecting the RSSI values by utilizing a mean smoothing method; substituting the smooth RSSI values into a polynomial obtained by fitting in order to obtain an environmental factor compensating factor EF; substituting the environmental factor compensating factor EF into a distance calculation formula: d=10^((ABS(RSSI)+EF-A)/(10*n)) in order to obtain distances d1, d2 and d3 between a locating point and the three base stations, wherein A is an average absolute value of received signal energy in 1 m, n is a signal energy attenuation coefficient, and RSSI is a value obtained after mean smoothing; and completing location by utilizing a centroid algorithm based on Euclidean according to the distances d1, d2 and d3 between the locating point and the three base stations. According to the method, the robustness and the precision can be improved.

Description

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AI Technical Summary

Benefits of technology

This technology helps improve the reliability and precision of triangular center location by optimizing its calculation process based on factors like temperature or humidity levels that may affect the geometry of objects being located within them. It also includes techniques such as adding extra data points from previous calculations to help further enhance this analysis. Overall, these technologies lead to improved object detection capabilities at different locations inside buildings while maintaining their effectiveness over time.

Problems solved by technology

The technical problem addressed in this patented text relating to indoor navigation systems involves improving their ability to locate objects accurately within specific areas while reducing power consumption compared to current techniques like global satellite positioning or ground proximate sensory networking. Current approaches require expensive infrastructure installation and may result in poor quality results if they don't work well outside buildings where electromagnetic waves travel around.

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