Wireless sensor network high-dimensional data completion method based on Hankel transformation

Abstract

The invention discloses a wireless sensor network high-dimensional data completion method based on Hankel transformation. The method comprises the steps of S1, modeling received wireless sensor network high-dimensional data into a tensor form chi; S2, counting effective element positions of the tensor data chi to form a tensor data support set omega; S3, performing TUCKER decomposition on the tensor data chi to obtain a kernel tensor of the tensor data chi, wherein chi = C * 1U (1) *... * iU (i)... * NU (N); S4, establishing a simple kernel tensor nuclear norm tensor completion model; and S5,based on the simple kernel tensor nuclear norm tensor completion model, carrying out Hankel transformation and variable separation, carrying out calculation filling on missing data through optimization of an alternate iteration method, and outputting a completion result. According to the method, data complementation can be rapidly and accurately carried out.

Description

technical field [0001] The invention relates to a high-dimensional data completion method of a wireless sensor network based on Hankel transformation, and belongs to the technical field of data completion. Background technique [0002] With the development of artificial intelligence technology, sensor applications are becoming more and more popular, such as various wearable devices, wireless communication devices, intelligent control devices, etc. Wireless sensor networks composed of various sensors have been widely used in national defense, military, and environmental monitoring. , biomedicine, intelligent transportation, emergency rescue, industrial safety, and supporting commercial applications. The wireless sensor network completes the perception, collection, processing and transmission of data information in the monitoring area through the cooperation of sensor nodes, and finally sends the relevant information to the data service center to provide data support for the s...

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Problems solved by technology

The file performs Hankelization operations on the observation data and the correlation matrix. If the data volume is large or the dimension is high, there will be problems such as complex operation and long time consumption.

The patent document with the application number "201810932079.X" discloses a "uniform area array tensor reconstruction method and signal source location method with partial array element damage", which uses a tensor completion method based on CP decomposition to realize the lack of received signals Data completion, its disadvantage requires a lot of calculation of the outer product of vectors and the sum of rank-1 tensors

The patent document with the application number "201811648489.8" discloses a "3D tensor completion method based on Capped nuclear norm". Completed low-rank tensor data, but in the process of constructing the capped nuclear norm, it needs to set the capped parameter, calculate the singular value of the tensor, compare the singular value and the size of the parameter and sum them up, etc., which has high calculation the complexity

The patent document with the application number "201810824663.3" discloses a "long-term spectrum prediction method based on image reasoning". By modeling the monitoring information of different frequency bands obtained from a complete monitoring day as a picture, the tensor is composed according to the time series of the monitoring day. The high-precision low-rank tensor completion method performs missing data completion and prediction, so it cannot consider the influence of noise on the data

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