Invention Grant
- Patent Title: Indoor target positioning method based on improved convolutional neural network model
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Application No.: US17542470Application Date: 2021-12-05
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Publication No.: US11943735B2Publication Date: 2024-03-26
- Inventor: Dengyin Zhang , Yepeng Xu , Yuanpeng Zhao , Yan Yang , Chenghui Qi
- Applicant: Nanjing University of Posts and Telecommunications
- Applicant Address: CN Nanjing
- Assignee: Nanjing University of Posts and Telecommunications
- Current Assignee: Nanjing University of Posts and Telecommunications
- Current Assignee Address: CN Nanjing
- Priority: CN 2110556627.5 2021.05.21
- Main IPC: H04W64/00
- IPC: H04W64/00 ; G06N3/08 ; H04W84/12

Abstract:
An indoor target positioning method based on an improved convolutional neural network (CNN) model includes acquiring and preprocessing target camera serial interface (CSI) data of a to-be-positioned target and matching the preprocessed target CSI data with fingerprints in a positioning fingerprint database to obtain coordinate information of the to-be-positioned target. The generation method of the positioning fingerprint database includes: collecting indoor WiFi signals by a software defined radio (SDR) platform to obtain indoor CSI data corresponding to the WiFi signals, and preprocessing the indoor CSI data; partitioning the preprocessed indoor CSI data into a plurality of data subsets through a clustering algorithm; training an improved CNN model by the data subsets to obtain a trained improved CNN model; and generating the positioning fingerprint database by the trained improved CNN model and the preprocessed indoor CSI data.
Public/Granted literature
- US20220386264A1 INDOOR TARGET POSITIONING METHOD BASED ON IMPROVED CONVOLUTIONAL NEURAL NETWORK MODEL Public/Granted day:2022-12-01
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