RFID indoor positioning method based on deep belief network

Abstract

The invention relates to a RFID indoor positioning method based on a deep belief network, belonging to the technical field of indoor positioning. The method comprises the following steps: establishinga RFID signal indoor propagation model; acquiring received signal intensity values of a reference tag at difference readers, and establishing a finger-printing database; performing feature extractionfor finger-printing data through DBN, wherein the extracted deep features are in one-to-one correspondence with position coordinates to form a new finger-printing database; extracting features for the received signal intensity values of a to-be-positioned tag at different readers through DBN, and performing similarity comparison with the features in the finger-printing database, thereby realizingposition estimation for the to-be-positioned tag. The method has the advantages that RFID indoor positioning is more accurate through similarity comparison between the features of the RFID to-be-positioned tag and the features in the finger-printing database, and for acquisition of the finger-printing data, the reference tag can be reused, and compared with a real-time positioning system, validity of the positioning system can be guaranteed all the time, and the system is enabled to have stability.

Description

technical field [0001] The invention belongs to the technical field of indoor positioning, and relates to an RFID indoor positioning system and an RFID indoor positioning in a complex indoor electric wave propagation environment, in particular to an RFID indoor positioning method based on a deep confidence network and a position fingerprint. Background technique [0002] Radio frequency identification technology (RFID) is a non-contact automatic data collection technology that uses space electromagnetic waves as the transmission medium. It has the advantages of small size, mature technology, fast speed, waterproof and antimagnetic, low power consumption, large capacity, no mechanical wear, and long life. , high precision and other advantages, its development has brought great convenience to people's life and production. [0003] Locating objects is one of the important applications of RFID systems, and has broad application prospects. When the existing RFID positioning meth...

AI Technical Summary

Problems solved by technology

When the existing RFID positioning methods deal with complex indoor environments, there are still some urgent problems to be solved in key issues such as positioning accuracy, positioning time, and adaptability, mainly reflected in: (1) Since RFID signals are easily affected by indoor multipath effect, shadow effect, and non-line-of-sight propagation, which make TOA, TDOA, AOA, and RSSI positioning methods based on ranging produce large positioning deviations

(2) Due to the continuous expansion of the positioning range and the increasingly complex positioning environment, the traditional position fingerprint positioning method has poor processing ability for large-scale fingerprint databases, and when the data in the fingerprint database is unbalanced, the system may suffer in terms of positioning accuracy and real-time positioning. disadvantage in performance

(3) At present, in the case of a large positioning range, traditional positioning systems such as LANDMARC require more reference tags. Once the reference tags lose their working ability, the positioning system will be paralyzed

Since the traditional RFID indoor positioning has the problems of poor positioning accuracy, poor environmental adaptability, and inability to handle large data in complex radio wave propagation environments, whether the above problems can be solved has become a key issue for RFID indoor positioning algorithms

At present, the combination of deep belief network and location fingerprint algorithm into RFID positioning system has not seen any relevant reports at home and abroad

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