Time sequence Bayesian compression sampling and signal decompression reconstruction method and data loss recovery method

Abstract

The invention provides a time sequence Bayesian compression sampling and signal decompression reconstruction method and a data loss recovery method. The method comprises the following steps: selectinga measurement matrix of signal compression sampling; designing a basic vector matrix, modeling the change sparsity of a signal and an adjacent time period thereof, solving the Bayesian probability ofa decompressed reconstructed signal in each time period, quickly optimizing and estimating hyper-parameters, diagnosing the reconstruction precision based on posteriori uncertain quantization, recovering the signal loss in the health monitoring wireless sensor, and the like. According to the method, a hierarchical sparse Bayesian learning modeling and solving algorithm is adopted, and the methodhas unique advantages in the aspects of embedding two sparse features of a signal and the time-varying sparse features of the signal, rapid solving of hyper-parameters, signal reconstruction robustness and signal reconstruction uncertainty quantification under a high compression rate and the like, and has good noise robustness.

Description

technical field [0001] The invention belongs to the technical field of signal processing and structural health monitoring, and in particular relates to a time-series Bayesian compressed sampling and signal decompression reconstruction method and a data loss recovery method. Background technique [0002] At present, under the joint action of factors such as environmental erosion, material aging, long-term load effects, and component defects, cumulative damage will inevitably occur in infrastructure, resulting in continuous reduction of its bearing capacity, continuous loss of functions, and even endangering the safe use of the structure. Therefore, ensuring the service safety and life of major projects is a major issue related to the national economy and people's livelihood. Structural health monitoring can effectively guarantee the safety, life and function of major projects by deploying large-scale, distributed sensor networks and data acquisition, transmission, management ...

AI Technical Summary

Problems solved by technology

[0006] At present, the research and application of Bayesian compression sampling in time series and image signals of structural health monitoring, in the robustness of the algorithm, the utilization of the structural characteristics of the signal itself, the processing ability and uncertainty of the time series signals acquired continuously over time There are still some deficiencies in aspects such as quantitative quantization, which make it difficult to meet the signal decompression accuracy of compressed sampling at a higher compression rate and difficult to maintain stability, and the superiority of compressed sampling technology has not been fully explored

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