RFID positioning method based on adaptive deep belief network

A technology of deep belief network and positioning method, which is applied in the field of RFID positioning based on adaptive deep belief network, and can solve the problems of large error and slow running speed.

Active Publication Date: 2017-10-13
合肥庐阳科技创新集团有限公司
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Problems solved by technology

[0005] The purpose of the present invention is to provide a method to ensure that the tags in the RFID system can be positioned quickly and accurately, which solves typical problems such as large errors and slow running speeds faced in RFID positioning, and is suitable for RFID positioning systems with higher precision requirements. RFID localization method based on adaptive deep belief network

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  • RFID positioning method based on adaptive deep belief network
  • RFID positioning method based on adaptive deep belief network
  • RFID positioning method based on adaptive deep belief network

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Embodiment Construction

[0041] Such as figure 1 Shown, a kind of RFID localization method based on self-adaptive deep belief network, this method comprises the steps of following order:

[0042] (1) Layout the position of the reader and the reference tag, and calculate the distance from the reference tag to the reader;

[0043] (2) The reader sends an electromagnetic wave signal to the reference tag and receives the signal strength RSSI value, and constructs a training sample vector matrix;

[0044] (3) Construct an adaptive deep belief network, using the RSSI value of each reference tag as an input value, and the distance d as an output value;

[0045] (4) Use the contrastive divergence algorithm to complete the pre-training of network parameters;

[0046] (5) Use the adaptive moment estimation method to adjust the weights of each layer of the deep learning network;

[0047] (6) The reader sends a signal to the tag to be tested and receives the RSSI value, and uses the deep belief network to pred...

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Abstract

The invention relates to an RFID positioning method based on an adaptive deep belief network. The method comprises a step of laying the positions of a reader and reference tags, and calculating distances from the reference tags to the reader, a step of allowing the reader to send electromagnetic wave signals to the reference tags and receive signal intensity RSSI values, and constructing a training sample vector matrix, a step of constructing the adaptive deep belief network, taking the RSSI value of each reference tag as an input value, and taking the distance d of each reference tag as an output value, a step of using a contrastive divergence algorithm to complete the pretraining of a network parameter, a step of using an adaptive moment estimation method to adjust a weight of each layer of a deep learning network, and a step of allowing the reader to send a signal to a tag to be measured and receive an RSSI value, and using the deep belief network to predict the position of the tag to be measured. According to the method, the adaptive deep belief network is used, a nonlinear relationship between a signal intensity value and the distance is constructed, a cross entropy cost function is used, and a problem of a slow learning rate is alleviated.

Description

technical field [0001] The invention relates to the technical field of target positioning of radio frequency identification technology, in particular to an RFID positioning method based on an adaptive deep belief network. Background technique [0002] At present, RFID technology is widely used in many occasions. For RFID tags, technology is required to accurately locate them. Tag positioning is mainly based on the nonlinear mapping model of signal strength (RSSI) and distance. The basic principle of RSSI positioning technology is that the attenuation of radio frequency signals is inversely proportional to the square of the distance, and the distance of signal transmission can be obtained by detecting the power strength of the received signal. . However, in the actual application process, due to the interference of the environment, this mapping relationship will fluctuate. Therefore, studying the quantitative relationship between RSSI and distance in the real environment ha...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S11/06
CPCG01S11/06
Inventor 袁莉芬戴文彬何怡刚张悦杜余庆朱国栋
Owner 合肥庐阳科技创新集团有限公司
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