A wifi indoor positioning method based on integrated hwknn
A technology for indoor positioning and location coordinates, applied in the field of wireless transmission, indoor positioning, and machine learning, which can solve problems such as uncertainty, decreased positioning accuracy, and unstable WiFi access points
Active Publication Date: 2020-06-09
SUN YAT SEN UNIV
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Problems solved by technology
However, due to the complexity of the indoor environment, the signal strength from different WiFi access points is often subject to different degrees of external interference, the relationship between signal strength and physical location is not a simple linear mapping relationship, and it cannot be determined at the node to be positioned Unstable WiFi access points, resulting in a decrease in positioning accuracy
Method used
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Embodiment Construction
[0066] Such as figure 1 As shown, a WiFi indoor positioning method based on integrated HWKNN includes the following steps:
[0067] 1) Build a reliable indoor WiFi fingerprint database, including the following steps:
[0068] 1-1) Every other step in the area to be positioned is used as a reference point;
[0069] 1-2) Each reference point performs multiple RSS signal data measurements, the physical address information of the wireless access point of each reference point, the average value of signal strength and the corresponding location information to construct a location fingerprint. If there is a signal loss, Set the value to 100, and then construct a location fingerprint library based on the location fingerprints corresponding to all reference points.
[0070] 2) Estimate the position coordinates of each access point and the indoor path loss model of each access point, including the following steps:
[0071] 2-1) Calculate the physical distance of the reference point c...
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The invention discloses a WiFi indoor positioning method based on integrated HWKNN. The method comprises the following steps: 1) constructing a reliable indoor WiFi fingerprint database; 2) estimating position coordinates of each access point and a corresponding indoor path loss model according to the established fingerprint database; 3) when a Euclidean distance between a to-be-positioned node and the fingerprint database is calculated, endowing a weight to a corresponding dimension by using the indoor path loss model obtained in step 2); 4) selecting K reference points with minimum distance for the Euclidean distance obtained in step 3), weighting the K reference points according to signal intensity similarity, and determining the position of the to-be-positioned node; and 5) using the method in step 2) to step 4) as a weak positioning method, establishing multiple weak positioning estimators by randomly selecting the size of the K value and the number of the access points so as to finally estimate the position coordinates of the to-be-positioned node. The positioning algorithm provided by the invention can effectively enhance the robustness of indoor positioning and finally realize accurate indoor positioning.
Description
technical field [0001] The invention relates to the fields of machine learning, wireless transmission and indoor positioning, in particular to a WiFi indoor positioning method based on integrated HWKNN. Background technique [0002] With the widespread application of mobile devices and the popularization of wireless networks, location-based services (LBS) can not only obtain user location information but also further mine user behavior information, so LBS shows good academic development prospects and broad market demand. . In the field of outdoor positioning, the Global Positioning System (GPS) has achieved precise positioning, but it is still difficult to achieve precise positioning in indoor environments. [0003] In the past decade, with the development of WiFi technology and the gradual increase of wireless access point coverage, a series of methods have been proposed for wireless local area network (WLAN) based indoor positioning. However, due to the complexity of ind...
Claims
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IPC IPC(8): H04W4/02H04W4/33H04B17/318H04B17/391
CPCH04B17/318H04B17/391H04W4/023H04W4/025
Inventor 陈绍建龙云亮
Owner SUN YAT SEN UNIV