Indoor positioning method based on wsn
An indoor positioning and inner area technology, applied in the directions of nan, electrical components, network topology, etc., can solve the problems of random fluctuation of received RSSI, reduced positioning accuracy, inability to communicate RSSI, etc., to avoid environmental uncertainty and jitter phenomenon, improve The effect of positioning accuracy
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Embodiment 1
[0026] This embodiment provides a WSN-based indoor positioning method, such as figure 1 As shown, the WSN-based indoor positioning method includes offline fingerprint collection and online positioning.
[0027] The off-line fingerprint collection includes:
[0028]Firstly, anchor nodes that meet the communication requirements of WSN are arranged in the indoor area to be located, so that the signal of the anchor node can be received at any position in the indoor area, and the indoor WSN network topology and route construction are completed. Specifically, arrange n WSN anchor nodes for the environment to be positioned (that is, the indoor area to be positioned), to ensure that the environment to be positioned is covered by WSN signals, and to ensure that any test point in the environment to be positioned can receive the anchor The number of nodes is at least 3. The WSN communication requirement is: the anchor node self-organizes to form a wireless sensor network in the indoor ...
Embodiment 2
[0077] This embodiment provides a WSN-based indoor positioning method, such as image 3 As shown, the difference between it and Embodiment 1 is that in step 2, after Kalman filtering is performed on the fingerprint, the similarity detection process is performed first, then the boundary detection process is performed, and finally the dimensionality reduction process is performed, that is, this embodiment and the implementation The difference of Example 1 is only that the sequence of processing methods adopted in the filtering process is different, and the principle is the same.
[0078] Wherein, the similarity detection processing described in this embodiment is: performing similarity detection processing on the optimal estimate of the n-dimensional RSSI observation vector after the Kalman filter processing, and the specific process is: for the similarity degree greater than or equal to the fixed threshold δ The RSSI observation component is discarded. Then perform boundary de...
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