Indoor positioning method and system by combining GRNN with genetic algorithm

The invention provides an indoor positioning method and system by combining a GRNN with a genetic algorithm. The method comprises the steps of enabling an RFID reader to move along a set track so as to obtain a plurality of tag signal intensity features of the RFID reader and a target tag and the po...

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Bibliographische Detailangaben
Hauptverfasser:	QIAN WEIHUA, TANG JINTAO, SHEN SHAOQIN, LIN FAN, ZHANG ZHIHONG, QIU YISHU, YANG LYUQING
Format:	Patent
Sprache:	chi ; eng
Schlagworte:	ANALOGOUS ARRANGEMENTS USING OTHER WAVES BASIC ELECTRONIC CIRCUITRY CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES ELECTRICITY GYROSCOPIC INSTRUMENTS HANDLING RECORD CARRIERS IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION ORRERADIATION OF RADIO WAVES MEASURING MEASURING DISTANCES, LEVELS OR BEARINGS NAVIGATION PHOTOGRAMMETRY OR VIDEOGRAMMETRY PHYSICS PRESENTATION OF DATA RADIO DIRECTION-FINDING RADIO NAVIGATION RECOGNITION OF DATA RECORD CARRIERS RESONATORS SURVEYING TESTING
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Beschreibung
Zusammenfassung:	The invention provides an indoor positioning method and system by combining a GRNN with a genetic algorithm. The method comprises the steps of enabling an RFID reader to move along a set track so as to obtain a plurality of tag signal intensity features of the RFID reader and a target tag and the position coordinates of the target tag in the moving process; establishing a GRNN network model, and adjusting hyper-parameters of the GRNN network model by adopting the genetic algorithm to obtain optimal hyper-parameters so as to train the GRNN network model according to the optimal hyper-parameters, the plurality of tag signal intensity features of the RFID reader and the target tag and the position coordinates of the target tag; and obtaining a plurality of tag signal intensity features of a to-be-positioned tag, and inputting the plurality of tag signal intensity features of the to-be-positioned tag into the trained GRNN network model for prediction to obtain position information of the to-be-positioned tag. The