Research On Self-positioning Algorithm Of Sensor Nodes To Target Tracking

The wireless sensor network consists of large number of inexpensive sensor nodes that can collect,transmit and process the information of the perceived object in real time and transmit the information to the user.It has been widely used in medical technology,military defense,public transportation and smart living.Target tracking is one of the most classic applications in wireless sensor networks.It is mainly divided into self-positioning of nodes and tracking of target nodes.This thesis mainly studies the self-positioning of nodes,and locates the unknown node by the known node.However,the selection of cluster head node and the active node has a great influence on the accuracy of the self-positioning of the nodes.Therefore,in order to improve the performance of node positioning,this thesis studies the selection of cluster head nodes,the number of active nodes and the deployment of node geometry.In view of the energy consumption problem,this thesis studies the selection of cluster head nodes and the number of active nodes.The cluster head node processes the data collected by the activation node and transmits to the base stations,thus the energy consumption is large,and the dynamic selection of the cluster head node can effectively balance the energy distribution in the network and prolong the network life cycle.Therefore,this thesis proposes a center-point energy equalization algorithm.At the initial moment,the cluster head node is randomly distributed;at other times,the center point of sensor nodes in the cluster is first calculated,and the node with a closer distance from the center point and higher residual energy is selected as the new cluster head node,and then the network is divided according to the Voronoi diagram to start a new round of tracking.In addition,the number of active nodes is adjusted according to the average measurement error of nodes.The method can dynamically adjust the number of active nodes by judging the difference between the predicted estimated value and the actual measured value at the previous moment.The simulation results show that the proposed method can effectively reduce the energy consumption of sensor nodes and prolong the service life of the network while ensuring the tracking accuracy.Aiming at the tracking accuracy problem,this thesis proposes an angle optimal measurement criterion.In the process of target tracking,the relative position between the active node and the target node has influence on the tracking accuracy.According to the angle optimal measurement criterion,the position of the active node is adjusted.Firstly,the information in the observation vector of the sensor node is calculated to judge the accuracy of the state correction of the target node.And the Cramer-Rao lower bound,which is the maximum value of the Fisher information matrix,is obtained.Then the angle relationship between the active node and the target node is achieved.Finally,the geometric position of nodes is deployed with the remaining energy of the node itself.The simulation results show that the tracking accuracy of the target node can be improved based on the angle optimal measurement criterion,and the number of active nodes can be further optimized.