Indoor positioning method based on fingerprint database secondary correction

Abstract

The invention discloses an indoor positioning method based on fingerprint database secondary correction, mainly solves a problem of poor positioning accuracy in an existing indoor positioning method. The indoor positioning method provided by the invention comprises the following steps: (1) selecting reference points, measuring the strength of received signals to be stored in a basic database; (2) performing primary clustering on the basic database; (3) removing a reference point which can cause large error in the basic database to update the basic database; (4) clustering the updated basic database to generate a new fingerprint database; (5) performing real-time positioning by use of the new fingerprint database to obtain a cluster matched with points to be positioned; and (6) acquiring location information of a reference point selected from the cluster matched with the points to be positioned, removing a point which is not fit with the whole, and performing compressed sensing on the processed point for precise positioning. According to the method provided by the invention, the positioning error is reduced and the positioning accuracy is improved; and the method can be applied to indoor positioning of a WiFi receiver.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and further relates to an indoor positioning method, which can be used in an indoor environment covered by a Wi-Fi signal to complete confirmation of current position information. Background technique [0002] At present, with the development of wireless networks and the widespread deployment of wireless local area networks, Wi-Fi-based indoor positioning technology has received extensive attention. In the indoor environment covering the Wi-Fi network, the location of the mobile user is determined by measuring the received signal strength RSS from the access point APs and combining with the collected signal strength database. This positioning algorithm based on location fingerprints is widely used because of its high positioning accuracy, full use of existing facilities, and little impact on users for upgrades and maintenance. In order to improve the positioning accuracy and effic...

AI Technical Summary

Problems solved by technology

Refer to the literature ExtendedKalmanFilterforRealTimeIndoorLocalizationbyFusingWiFiandSmartphoneInertialSensors, ZADeng, YHu, JYu, ZNa, mainly use the gyroscope and accelerometer of the mobile phone to estimate the relative displacement through footstep detection and estimation of the direction of travel. If the user's initial position is known, it can be estimated after moving a short distance Absolute position, but as the moving distance increases, the drift error will accumulate, especially when the mobile phone uses a cheap and noisy sensor, this phenomenon will be more serious; there is also a classic indoor positioning method based on the K-means clustering algorithm , the idea of ​​K-means clustering algorithm is: determine the exact number K to be clustered, and randomly select K objects, each representing the mean or center of a sub-category, for each remaining object, according to its relationship with each sub-category The distance from the center divides it into the subclass with the shortest distance. The disadvantage is that the clustering process only considers the Euclidean distance without taking into account the signal strength information of the selected reference point, which leads to a large error in the positioning stage based on the signal strength. deviation

[0004] In the process of positioning, the above methods ignore the deviation of the clustering effect caused by the reference point itself, the decrease of positioning accuracy, and the situation that the reference point selected in the positioning stage does not match the overall situation.

Due to the influence of external factors such as signal access point distribution and building structure, some reference points cannot provide useful basic information. In this case, if these reference points are not removed, the selection of these reference points during the positioning process will directly lead to The positioning accuracy decreases, so it is necessary to make error points for the basic database formed by the reference points for database correction

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