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

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;...

AI Technical Summary

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 greatly improves the smoothness of position estimation

The results of indoor positioning experiments in a large number of real environments show that the method of the present invention can effectively solve the problem of indoor positioning in a large area, and compared with the traditional WKNN method, the positioning accuracy is greatly improved

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