Patent ID: 11943735
Assignee: nan
Field: Computer technology (Electrical engineering)
Classification: CPC G  H  Y | IPC G  H

Claim 4:
5. A method, comprising:
acquiring and preprocessing target camera serial interface (CSI) data of a to-be-positioned target; and
matching preprocessed target CSI data with fingerprints in a positioning fingerprint database to obtain coordinate information of the to-be-positioned target;
wherein:
a generation method of the positioning fingerprint database comprises:
collecting indoor WiFi signals by a software defined radio (SDR) platform to obtain indoor CSI data corresponding to the indoor 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 convolutional neural network (CNN) model by the plurality of 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;
wherein the improved CNN model comprises five convolution layers, a first fully connected layer, and a second fully connected layer connected in sequence; the five convolution layers comprise 16, 32, 64, 64 and 128 kernels, respectively; the first fully connected layer is configured to flatten outputs of a fifth convolution layer, and the second fully connected layer is configured to output partitioned CSI data;
wherein training the improved CNN model by the data subsets comprises:
standardizing each indoor CSI data in each data subset:, x
       p
      
      =
      
       
        
         var
         p
        
        -
        
         mean
         (
         
          var
          p
         
         )
        
       
       
        total
        (
        
         var
         p
        
        )
       
      
     
     ;
    
   
   
    
     (
     3
     )
    
   
  
 

where xp represents a pth indoor CSI data after being standardized, varp represents the pth indoor CSI data in the data subset, mean( ) is a function MEAN, total( ) is a function SUBTOTAL, p=1, . . . , n, where n is a number of indoor CSI data in each data subset;
initializing model parameters of the improved CNN model inputting the indoor CSI data standardized in each data subset into the five convolution layers of the improved CNN model to obtain the convoluted indoor CSI data, and extracting features of the convoluted indoor CSI data;
batching, max-pooling and activating the features of the convoluted indoor CSI data to obtain mappings of the convoluted indoor CSI data;
classifying the mappings of the convoluted indoor CSI data by the second fully connected layer of the improved CNN model to obtain coordinate information corresponding to each indoor CSI data in each data subset;
calculating losses of the improved CNN model b a loss function based on a default real coordinate label and the coordinate information output by the improved CNN model; and
updating the model parameters of the improved CNN model based on the losses, and processing, by an updated improved CNN model, the indoor CSI data standardized until the losses converge, to obtain the trained improved CNN model.