Research On Anti Unreliable Measurement Information Compression And Estimation Methods In Industrial Sensor Network

With the development of wireless communication technology,the application of industrial sensor network is mainly reflected in the field of industrial wireless sensor network.Industrial wireless sensor network technology plays an important role in the field of industrial information,which is the main embodiment of the application of wireless sensor network in the industry.However,the noisy industrial environment,the mutual interference of industrial equipment,and the blocking of wireless communication links and other factors result in the data transmission of industrial wireless sensor network becomes unreliable.And data packet loss and other problems often occur in the process of network data transmission.The thesis takes the reliability of data transmission in industrial wireless sensor network as the research target,focus on the monitoring function of sensor nodes and starts from the design of state estimation method.For the packet lossy network,the following aspects are researched:1.For a packet lossy network with single sensor and single communication channel,in order to compensate for the lost packets,a scheme of data preprocessing at the sensor end is proposed.The measurement information obtained by the sensor in a certain period is combined to achieve the purpose of dimension compression,so that the measurement information dimension becomes a scalar.Then,the state estimation method under the minimum mean square error criterion is proposed.The convergence of the estimation method with packet loss is analyzed,and the convergence condition of the estimation error covariance is obtained.Further,the simulation results indicate that the proposed estimation method can effectively compensate the lost packets,reduce the estimation error and improve the reliability of data transmission.2.For the multi-sensors network with packet loss,because the data fusion of sensor network can reduce the loss of energy,the thesis studies the distributed fusion estimation method which is the special case of data fusion.Under the linear minimum variance criterion,assuming that the packet loss model of each channel is independent,the local optimal estimator and the local optimal estimation error covariance are obtained by the estimation method under the condition of measurement information dimension compression,and then the fusion estimation method is carried out.Then,three fusion estimation methods are proposed: matrix weighted fusion estimation based on measurement information dimension compression,batch Covariance Intersection fusion estimation based on measurement information dimension compression,and sequential Covariance Intersection fusion estimation based on measurement information dimension compression.3.Combined with specific system parameters,three kinds of distributed fusion estimation methods based on measurement information dimension compression and distributed fusion estimation methods based on the intermittent Kalman filter are compared by simulation.It is proved that the estimation accuracy of the proposed methods in this thesis are better,which can reduce the estimation error and improve the reliability of network data transmission.