WSNs Node Location Algorithm Based Null-space Tuning

At present,wireless sensor networks are widely used in the fields of remote control,target tracking,seabed detection and virtual scene layout.In particular,location-based service has become an important research direction of wireless sensor network node location.Compressed sensing makes use of the prior knowledge that the signal can be expressed sparsely.Therefore,it is applied to the localization technology to carry out in-depth research.In order to solve the problem of low positioning accuracy and location delay in wireless sensor networks,an improved boundary box algorithm is proposed in this dissertation to reduce the location area of unknown nodes.Then a reconstruction algorithm based on null space tuning optimization based on hard threshold and feedback is used to reconstruct the coordinates of the nodes to be tested.The maincontents are as follows:1.The null space tuning and reconstruction algorithm based on hard threshold and feedback is applied to WSNs to improve the accuracy of node location.At present the greedy algorithm commonly used in compressed sensing reconstruction algorithm has many iterations and takes a long time.In this dissertation null space tuning reconstruction algorithm is applied to node location which can reconstruct the location of nodes under test in a short execution time.Moreover,the iterative cycle is less,so the algorithm has obvious effect in the location environment with large amount of data,and its reconstruction quality and time are better than other algorithms.2.An improved Bounding Box algorithm is proposed to reduce the region to be measured at the node to be tested.In the current node localization algorithms based on compression perception,the entire region to be tested is usually used as the estimation range of unknown nodes,which makes the localization error larger.In this dissertation,the minimum rectangular boundary box algorithm can effectively reduce the measurement area of the node to be tested,and make the area to be measured of the node much smaller than the whole region to be tested,thus improving the accuracy of the location.3.The results of Matlab simulation show that the minimum rectangular bounding box method can effectively frame the measurement range of the nodes to be tested.The simulation results show that the average localization error of the proposed algorithm is lower than that of the SP-based localization algorithm,the OMP-based localization algorithm and the CoSaMP-based localization algorithm,and the execution time of the algorithm is shorter.This algorithm has the advantages of reducing the execution time of the algorithm,improving the accuracy of location and improving the efficiency of operation.With the expansion of the number of nodes to be tested,the experimental results show that the average location error of the proposed algorithm tends to be stable with the increase of the number of nodes to be tested,which shows that the algorithm is also suitable for the location of large amounts of data,and the method has a certain extension value.