The Application Of Compressed Sensing In Shock Wave Sensor Network

The shock wave produced by guns and other weapons will have adverse effects on operators and surrounding equipment.Therefore,accurate acquisition of shockwave signals can provide quantitative and reliable data for the evaluation of damage and weapon performance.Shock wave signal has short duration of high frequency information and burst characteristics.In order to ensure the integrity of information,it is necessary for nodes in the sensor network to maintain high sampling rate to capture it.However,the continuous high sampling rate will lead to excessive redundant data,which will put pressure on the storage space of sensor nodes and the data transmission performance of sensor network.Shock wave signals have the characteristics of energy concentration and meet the sparse requirement of compressed sensing technology for the measured signals.In order to solve the contradiction between redundant data and limited network resources in shockwave test system,this paper applies compressive sensing technology to shockwave sensor network.The main research contents are as follows:In order to reduce the pressure of redundant data on the storage space of sensor network nodes,this paper improves the sparse method of shockwave signal,and proposes a dictionary construction method based on the characteristics of shockwave signal.The shock wave signal is divided into transient and non-transient parts,the main information of each part is extracted,the corresponding sample set elements are adjusted,and the dictionaries of the two features are iteratively learned by K-SVD,so that the sparse vectors converge to the local characteristics,avoiding the phenomenon that the traditional redundant dictionary reduces the dictionary sparse performance and energy dispersion to take care of the global information of the signal.Simulation results show that the compression rate of the improved algorithm is 25% and 16.7% higher than that of the dictionary construction algorithm based on K-SVD when the data collected by 5psi and 50 psi sensors are compressed.In order to improve the data transmission performance of sensor network and reduce the energy loss of nodes in the network,this paper optimizes the energy consumption of the network through the combination of compressed sensing and routing algorithm.Based on sparse random projection theory,an improved random walk routing algorithm based on compressed sensing is proposed.By adding distance weight factor,the node selection strategy and trend of random walk routing are limited,and the relative distance of data transmission is reduced,so as to improve the energy efficiency of nodes in the network.The experimental results show that the improved algorithm is 23.75% better than the traditional random walk routing algorithm based on compressed sensing in optimizing network energy consumption.Through the above two aspects,the existing problems in the distributed shockwave signal wireless transmission system are solved,the storage pressure of network nodes is reduced and the energy consumption of the nodes in the shockwave wireless sensor network is optimized.