Multisource signal collaborative compressed sensing data recovery method

Abstract

The invention discloses a multisource signal collaborative compressed sensing data recovery method comprising the steps as follows: 1, an aggregation node in a wireless sensor network acquires historical data of each sensor node, and computes a sparse structure information matrix by using the historical data and a first optimization equation; 2, the aggregation node receives a compressed sensing measurement result matrix that is to be processed and is transmitted by each sensor node; and 3, the aggregation node performs a data recovery operation on the compressed sensing measurement result matrix to be processed of each sensor node by using the sparse structure information matrix computed in the step 1, wherein the recovery data is a recovery result corresponding to current data to be transmitted of each sensor node after compressed sending measurement. According to the method provided by the invention, the sparse structure information matrix is trained by the compressed sensing measurement result matrix of the historical data, and the training process considers information loss during the compressed sensing measurement process, and thus the subsequent data recovery accuracy is improved.

Description

technical field [0001] The invention belongs to the field of wireless sensor networks, and in particular relates to a multi-source signal cooperative compression sensing data recovery method. Background technique [0002] In applications such as wireless sensor networks, nodes are usually powered by batteries, with limited energy supply, and signals are usually transmitted wirelessly, with high transmission overhead. Therefore, energy saving and consumption reduction are important research contents of wireless sensor networks. Data compression technology is one of the most important techniques to reduce data transmission overhead. Compressed sensing is a new type of compression technology, which integrates signal acquisition and compression, breaks through the limitations of traditional Nyquist theory, and can greatly reduce sampling and computing overhead. [0003] In application scenarios such as sensor networks, there are usually multiple nodes. There are certain correl...

AI Technical Summary

Problems solved by technology

Therefore, the accuracy of existing data recovery methods needs to be further improved

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