Indoor object localization method based on knn with neighbor weighted adaptive k-value

Abstract

The present invention provides a KNN indoor target positioning method based on the neighborhood weighted self-adaptive k value, which includes the following steps: Step 1: constructing a location fingerprint database using RSS data collected by RFID equipment at each location in the room, the location fingerprint database includes a location set P and the signal strength value R; step 2: use the RFID card reader antenna to collect the corresponding signal strength value of the target to be positioned, and then use the KNN improved algorithm to match the signal strength value with the location fingerprint database to obtain the fingerprint with the highest matching degree, and put the The fingerprint mapping position is used as the current position of the target to be located. The KNN improved algorithm obtains the optimal k value corresponding to the fingerprint information of the target to be located by calculating the correlation between the online measurement RSS data set and the fingerprint data set in the location fingerprint database, and uses the proximity weighting method to reduce the matching error probability as much as possible. The simulation results show that the matching accuracy of the improved algorithm is at least 7.1 times higher than that of KNN and WKNN.

Description

technical field [0001] The invention relates to position fingerprint positioning technology, in particular to a KNN indoor target positioning method based on adjacent weighted self-adaptive k value. Background technique [0002] With the maturity of the mobile Internet, location-based mobile application services are becoming more and more common in people's daily lives. In the outdoor environment, applications such as Internet of Vehicles, shared bicycles, and smart logistics are all based on location positioning to provide services for people. In the indoor environment, services such as indoor navigation, elderly care and personnel care have greatly facilitated people's daily necessities of life. At the same time, relevant literature shows that people spend more than 80% of their time indoors, which makes people's demand for indoor positioning services higher and higher. Therefore, indoor wireless positioning service has become a hotspot of extensive research at present. ...

AI Technical Summary

Problems solved by technology

The trilateration positioning method has low complexity and low calculation amount, but it is limited by the performance of the RFID itself, and the positioning accuracy is not high

The k-nearest neighbor algorithm is generally used as the matching algorithm in the position fingerprint positioning method, but in the actual application process, the algorithm has the problem of being unable to adapt to the characteristics of the data sample due to factors such as the fixed value of k and the choice of distance measurement.

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