Sliding time window based WLAN (Wireless Local Area Network) indoor WKNN (Weighted K Nearest Neighbors) tracking method
A sliding time window, indoor positioning technology, applied in the field of pattern recognition, can solve the problems of severe jitter of estimated position coordinates and uneven estimated trajectory.
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specific Embodiment approach 1
[0024] Specific implementation mode one: according to the instructions attached figure 1 , 2 , 3, 4 and 5 specifically describe the present embodiment, the WLAN indoor WKNN tracking method based on the sliding time window described in the present embodiment, its tracking process is:
[0025] Step 1: Set N evenly in the indoor positioning area of the WLAN target terminal RP reference points, and arrange N in the indoor positioning area c APs, so that each reference point at least collects a signal strength RSS value from one AP;
[0026] Step 2: Select a reference point as the coordinate origin O to establish a two-dimensional rectangular coordinate system, and obtain N RP The coordinate positions of each reference point in the two-dimensional Cartesian coordinate system, and based on the coordinate position of each reference point and the signal strength RSS value from each access point AP collected by each reference point, a location fingerprint database is established ...
specific Embodiment approach 2
[0039] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in this embodiment, in step 38, the final estimated position required when obtaining the smooth estimated motion trajectory of the target terminal also includes Final estimated position at non time CR ( non ) = ( CR ( non ) x , CR ( non ) y ) = ( non - b a - b ( CR ( a ...
specific Embodiment approach 3
[0040] Specific implementation mode three: according to the instructions attached Image 6 with 7 Describe this implementation mode in detail. This implementation mode is a further description of the specific implementation mode 1 or 2. In the specific implementation mode 1 or 2, in step 38, the position dispersion threshold D T = max g = 1 N text { Σ p = 1 N rand Σ q = 1 N rand ( x g , p ...
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