WKNN indoor positioning method based on spatial feature partitioning and front point constraint

A spatial feature and indoor positioning technology, applied in positioning, transmission monitoring, measuring devices, etc., can solve problems such as narrowing the candidate range of reference points, and achieve the effect of solving indoor positioning problems, improving positioning accuracy, and narrowing the candidate range

Inactive Publication Date: 2018-04-20
BEIHANG UNIV
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Problems solved by technology

This method solves the problem of full-area coverage of the fingerprint database by dividing a large area into multiple partitions according to its spatial characteristics; and narrows down the candidate range of reference points by considering the spatial constraint relationship between the front and rear positions of pedestrians , which gre

Method used

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  • WKNN indoor positioning method based on spatial feature partitioning and front point constraint
  • WKNN indoor positioning method based on spatial feature partitioning and front point constraint
  • WKNN indoor positioning method based on spatial feature partitioning and front point constraint

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Embodiment Construction

[0098] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0099] The present invention proposes a WKNN indoor positioning method based on spatial feature partitioning and previous point constraints. The flow chart of the spatial feature partitioning part is as follows figure 1 As shown, the flow chart of the part of the previous point constraint method is as follows figure 2 Shown, the method flowchart of the present invention is as image 3 shown.

[0100] The present invention is a WKNN indoor positioning method based on spatial feature partitions and previous point constraints, such as image 3 As shown, it includes the following five steps:

[0101] Step 1: Divide the partitions

[0102] Assuming that there are m reference points in the target area, the position coordinates of the i-th RP are (x i ,y i ), at which point n can be obtained i AP information, the original data of the WIFI signal is

[0103]

[0...

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Abstract

The invention provides a WKNN indoor positioning method based on spatial feature partitioning and front point constraint, and the method comprises the following steps: 1, dividing a region into subregions; 2, extracting a subregion identification sequence; 3, carrying out the subregion discrimination; 4, calculating a signal spatial distance; 5, obtaining position estimation through weighting. Through the steps 1-5, the method provided by the invention is executed, and enables a large target region to be divided into a plurality of subregions according to the spatial features. Meanwhile, a combined partitioning criterion of the recognition sequence and Euclidean distance is introduced, thereby solving a problem that a fingerprint database cannot achieve the coverage of the whole region. The method gives consideration to the spatial constraint relation of the positions of a pedestrian at adjacent moments, thereby reducing the screening range of final reference points, and greatly improving the precision of position estimation.

Description

【Technical field】 [0001] The invention provides a WKNN indoor positioning method based on spatial feature partitions and front-point constraints, and belongs to the technical field of indoor positioning. 【Background technique】 [0002] At present, indoor positioning has not yet formed a unified and mature solution. However, various indoor positioning methods based on different application scenarios and environments have been continuously proposed, including: Pedestrian Dead Reckoning (PDR) algorithm based on Micro-Electro-Mechanical System (MEMS) , Indoor positioning methods using radio frequency identification (Radio Frequency Identification, RFID) technology, indoor GPS positioning methods based on pseudo-satellite networks, and indoor positioning methods based on WIFI signals. However, the above methods all face various problems: due to the accumulation of MEMS errors, the positioning accuracy of the PDR algorithm will gradually decrease, so that it will fail completely;...

Claims

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Application Information

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IPC IPC(8): H04W64/00H04B17/318H04L29/12G01S5/02
CPCH04B17/318H04W64/006G01S5/0257H04L2101/622H04L2101/69
Inventor 傅惠民杨海峰张勇波王治华肖梦丽崔轶
Owner BEIHANG UNIV
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