Apparatus and method for precise positioning based on deep learning
View Patent ↗Disclosed herein are an apparatus and method for precise positioning based on deep learning. The method performed by the apparatus includes setting a collection location and a collection environment, collecting wireless signal data based on the collection location and the collection environment, generating a magnitude map image for training from the wireless signal data, and generating a positioning DB model by learning the image characteristics of the magnitude map image for training through deep-learning-based training.
1. A method for precise positioning based on deep learning, performed by an apparatus for precise positioning based on deep learning, comprising:
setting a collection location and a collection environment and collecting wireless signal data based on the collection location and the collection environment; and
generating a magnitude map image for training from the wireless signal data and learning image characteristics of the magnitude map image for training through deep-learning-based training, thereby generating a positioning DB model,
wherein the wireless signal data is a Channel State Information Reference Signal (CSI-RS), and
wherein the magnitude map image is a graph that represents magnitude data of a frequency response for each subcarrier of the CSI-RS.
2. The method of claim 1 ,
wherein the magnitude map image is the graph in which magnitude data of a frequency response for each OFDM symbol index is further considered, in addition to the magnitude data of the frequency response for the each subcarrier.
3. The method of claim 2 ,
wherein the magnitude map image is the graph represented as multiple graphs so as to correspond to CSI-RSs collected from multiple channels in the collection location.
4. The method of claim 3 ,
wherein generating the positioning DB model is configured to generate the positioning DB model by learning the image characteristics of the magnitude map image for training and information about the collection location of the wireless signal data through the deep-learning-based training.
5. The method of claim 4 ,
wherein the image characteristics are a number of graphs and patterns thereof.
6. An apparatus for precise positioning based on deep learning, comprising:
one or more processors; and
executable memory for storing at least one program executed by the one or more processors,
wherein the at least one program is configured to set a collection location and a collection environment and collect wireless signal data based on the collection location and the collection environment; and
wherein the at least one program is configured to generate a magnitude map image for training from the wireless signal data and generate a positioning DB model by learning image characteristics of the magnitude map image for training through deep-learning-based training,
wherein the wireless signal data is a Channel State Information Reference Signal (CSI-RS), and
wherein the magnitude map image is a graph that represents magnitude data of a frequency response for each subcarrier of the CSI-RS.
7. The apparatus of claim 6 ,
wherein the magnitude map image is the graph in which magnitude data of a frequency response for each OFDM symbol index is further considered, in addition to the magnitude data of the frequency response for the each subcarrier.
8. The apparatus of claim 7 ,
wherein the magnitude map image is the graph represented as multiple graphs so as to correspond to CSI-RSs collected from multiple channels in the collection location.
9. The apparatus of claim 8 ,
wherein the at least one program determines a similarity of image characteristics between the magnitude map image and each of previously learned magnitude map images included in the positioning DB model and estimates the location information pertaining to the wireless signal data from information about a collection location of a previously learned magnitude map image having a highest similarity.
10. The apparatus of claim 9 ,
wherein the image characteristics are a number of graphs and patterns thereof.