Wifi fingerprint database construction method based on multi-sensor device
A multi-sensor and database technology, applied in measuring devices, instruments, surveying and mapping and navigation, etc., can solve the problem of consuming a lot of time and manpower in building and updating WiFi databases
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Embodiment 1
[0049] Such as figure 1 Shown is a method for constructing a wifi fingerprint database based on a multi-sensor device provided by an embodiment of the present invention, the multi-sensor device includes an accelerometer, a magnetometer, a gyroscope, a wireless signal transceiver and a processor, and the method includes :
[0050] In step 201, the navigation state of the multi-sensor device is initialized, wherein the navigation state includes initial position information, initial speed information and initial attitude information.
[0051] Among them, the multi-sensor device can use physical sensors or technologies, such as gyroscopes, accelerometers, magnetometers, barometric altimeters, GPS, etc., and can also add virtual sensors, such as a virtual gyroscope composed of a group of photos, a virtual Odometer, landmark location provided by map information, etc. The device referred to in step 1 can be a professional measurement or navigation system or platform built using the...
Embodiment 2
[0089] This embodiment further proposes a method to improve the implementation efficiency based on the implementation of the first embodiment, specifically: if there is a sensor that can directly provide the location information of the device, for example, the device receives GPS, pseudolite or other positioning technology during the navigation process. signal, and the positioning accuracy meets the requirements, you can choose to skip step 203 to calculate the position information of the multi-sensor according to the navigation algorithm, and directly use the position information to construct and update the WiFi fingerprint database. Such as figure 2 shown.
Embodiment 3
[0091] This embodiment provides a specific reasoning method for step 203 in Embodiment 1, specifically including:
[0092] First, the inertial navigation Kalman filter algorithm is used to calculate the attitude information of the device in real time. The Kalman filter system model is:
[0093] δ r · n δ v · n ψ · = - ω e ...
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