Coarse and fine matching positioning method based on WIFI address and field intensity

Abstract

The invention provides a coarse and fine matching positioning method based on a WIFI address and field intensity. The method comprises the following steps: 1, carrying out quadrilateral grid or triangular mesh fingerprint point layout; 2, acquiring and preprocessing data, and establishing a fingerprint database; 3, performing clustering and blocking processing on the fingerprint database, and calculating the class center of each fingerprint database class, including a class center signal intensity vector and a corresponding class address vector; 4, roughly matching the access point address information of a real-time WIFI signal with the address vector of each class to realize rough class range estimation; 5, calculating the distance between the real-time WIFI signal intensity value and theWIFI signal intensity center vector in the class obtained after rough matching and the probability, and accurately determining the class so as to enhance the class matching accuracy; and 6, in the accurately determined class set, calculating the final position of the target through a WKNN algorithm. According to the invention, the workload of fingerprint acquisition and the positioning calculation amount can be reduced, and the WIFI positioning precision is improved.

Description

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AI Technical Summary

Benefits of technology

This patented technology improves on previous methods where samples were taken from different locations but it still requires significant effort because they had to be located at specific positions beforehand. However this was done without considering factors like location precision. To solve these issues, an algorithm called Triangle Network (TN) has been developed based upon signals collected over time. It involves grouping small samples into smaller groups within certain areas around each other's corner points. These clusters help reduce processing load while also providing accurate estimates about their attributes such as geographic coordinates. Additionally, TN can use both average and peak values to calculate distances between pairs of nodes, making calculations faster compared to older techniques. Overall, this innovation allows us to make better quality measurements quickly and efficiently.

Problems solved by technology

Technically speaking, there exist challenges when trying to locate wireless devices within an area that has many different types of sources or interference from each others. These issues make accurate localization even harder due to varying levels of blockage and complexity introduced during this process. Current techniques involve either using radioactive markers or hand-scored maps, but these have limitations because they cannot provide realistically relevant results over time.

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