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Large-scale MIMO dynamic environment fingerprint positioning method based on domain adaptive network

A domain-adaptive and dynamic environment technology, applied in specific environment-based services, biological neural network models, location-based services, etc., can solve problems such as high cost of location fingerprint database, failure of location fingerprint database, failure to meet positioning requirements, etc. , to achieve the effects of alleviating fuzzy classification and reducing matching results, good effect and easy implementation

Pending Publication Date: 2022-04-01
SOUTHEAST UNIV +1
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  • Claims
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AI Technical Summary

Problems solved by technology

However, there are still deficiencies: the fingerprint method has high requirements on the environment and cannot meet the positioning requirements in a dynamic environment.
When the multi-path environment in the positioning area changes (for example: parking of outdoor vehicles and movement of indoor homes, etc.), the wireless fingerprint of the same location changes during the online phase, resulting in the invalidation of the location fingerprint database collected in the original environment.
In the new environment, the mapping relationship between wireless fingerprints and locations has changed, so that the positioning model established by the initial location fingerprint database training is no longer applicable to the new environment, and the cost of re-collecting samples to update the location fingerprint database is too high

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  • Large-scale MIMO dynamic environment fingerprint positioning method based on domain adaptive network
  • Large-scale MIMO dynamic environment fingerprint positioning method based on domain adaptive network
  • Large-scale MIMO dynamic environment fingerprint positioning method based on domain adaptive network

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Embodiment Construction

[0069] In order to make those skilled in the art better understand the present invention, the implementation process of the technical solution is further described in detail below with reference to the accompanying drawings.

[0070] like figure 1 As shown, the large-scale MIMO (multiple input multiple output) dynamic environment fingerprint positioning method based on the domain adaptive network disclosed in the embodiment of the present invention mainly includes two parts: a training phase and a positioning phase.

[0071] Training phase: Divide the two-dimensional positioning area into uniform N block =N 1 ×N 2 grid blocks and numbered, N 1 ,N 2 are the total number of rows and columns for dividing grid blocks, and record the center coordinates of each grid block in corresponds to the nth 1 line n 2 The horizontal and vertical coordinates of the center position of the column grid block. In the initial environment, K sample points are divided at equal intervals, a...

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Abstract

The invention provides a large-scale MIMO (Multiple Input Multiple Output) dynamic environment fingerprint positioning method based on a domain adaptive network, which comprises the following steps of: training a positioning model by combining a small number of unsupervised samples in a current environment on the basis of an initial environment position fingerprint database by adopting a domain adaptive learning mode; the working capability of the model in the current environment can be improved, and the trained model has relatively high robustness in the current environment; according to the scheme, the positioning precision improvement effect in the dynamic environment is good, the positioning model in the current environment can be trained and obtained through a small amount of label-free data in the current environment, and the problems that the positioning model loses efficacy and the resampling cost is high after the environment changes can be relieved to a certain extent.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a large-scale MIMO (multiple input multiple output) dynamic environment fingerprint positioning method based on a domain adaptive network. Background technique [0002] With the rapid development of Internet of Things technology, the demand for Location Based Service (LBS) has increased significantly, so that precise positioning technology has received extensive attention. At present, the main positioning technology is divided into geometric method and fingerprint method. The geometric method usually uses multiple base stations to receive the Angle of Arrival (AOA), Time of Advent (TOA) and Received Signal Strength (RSS) of the Line of Sight (LOS) of the user signal. Measured values ​​for co-location. However, in complex environments such as tall buildings and indoors, the received signal contains a lot of Non-line of Sight (NLOS) interference or even...

Claims

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Application Information

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IPC IPC(8): H04W64/00H04W4/021H04W4/33H04L25/02H04B7/0413G06K9/62G06N3/04G06N3/08
Inventor 潘志文蒋志函刘楠尤肖虎
Owner SOUTHEAST UNIV
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