Indoor fingerprint quick positioning method based on support vector regression
A technology that supports vector regression and positioning methods, applied in location-based services, measurement devices, radio wave measurement systems, etc., can solve the problems of huge fingerprint matching database, original data interference noise, poor real-time performance, etc., to reduce the complexity of matching Accuracy, reducing noise interference, and improving real-time performance
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Embodiment 1
[0053] This embodiment discloses a method for quickly positioning indoor WLAN fingerprints based on support vector regression, which includes the following steps: it consists of two parts: an offline stage and an online stage, wherein the offline stage is responsible for establishing a feature fingerprint database of reference points and training feature fingerprints and reference points. The relationship model between point positions; in the online stage, the relationship model between the feature fingerprint and the reference point position obtained in the offline stage is used for rough positioning, and then the weighted K nearest neighbor algorithm is used for accurate positioning. The block diagram of the whole system is as figure 1 shown.
[0054] The coarse positioning adopts svm coarse positioning, and the accurate positioning adopts knn fine positioning.
[0055] The present invention is different from the traditional positioning method using the fingerprint database...
Embodiment 2
[0083] like Figure 1-3 As shown, this embodiment discloses a fast indoor WLAN fingerprint positioning method based on support vector regression. As a further embodiment of Embodiment 1, specific numerical values are used to illustrate, and the steps are as follows:
[0084] Step 1: Construct the feature fingerprint database in the offline stage;
[0085] Step 1.1: Determine the position of the reference point according to the principle of uniform sampling according to the indoor map, according to image 3 As shown, 5 WLAN wireless APs are placed and 23 reference points are selected for illustration. Different reference points can be selected for different scenarios in actual applications. At each reference point position, the signal strength RSS of all WLAN wireless APs around is collected 100 times, which can be expressed as a set L={l 1 ,l 2 ,...,l N}, where l i Represents the fingerprint information of the i-th reference point, Indicates the signal strength of t...
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