Research On Target Detection Technology In Wireless Sensor Network

Wireless Sensor Network(WSN)is a self-organizing network system made up of a large number of cooperative sensors and sensing devices that have computing,communication,and sensing capabilities,allowing for the integration of communication,perception,and computing.These sensors operate autonomously and are deployed in the monitoring area to detect events and effectively monitor and track them.The research field of WSN is expanding due to the rapid development of WSN and the in-depth development of various large-scale application scenarios involving WSN.Network coverage optimization technology is an important part of WSN technology.Node positioning,network coverage control,and other hot technologies are included in the technology.The premise for ensuring WSN network connectivity and full coverage of the region is network deployment.As a result,it can better cover the area to be detected by designing a scientific deployment algorithm to adjust the position of the sensing device and optimize the network topology.This ensures that a reliable data source for network target detection is available.At the same time,WSN data fusion technology is an important means of target detection.One of the most important fusion results people pay close attention to is the target’s location information,which can be quickly obtained using an efficient fusion positioning algorithm.In the process of target detection,data fusion positioning and network deployment complement each other,so how to quickly obtain the optimal solution for the target location through data fusion positioning technology under the premise of a deterministic network is a significant research problem.In view of the above problems and challenges,this thesis focuses on the deployment and data fusion positioning in target detection for WSN.In the WSN,network coverage means how well an area of interest is being monitored by the deployed network.It depends mainly on the sensing model of nodes.Under certain constraints,the network perception model is reconstructed in this thesis by analyzing the influence of terrain factors and combining it with the signal transmission loss model,and then the theoretical maximum perception distance is calculated.Based on this,a network node deployment algorithm is proposed to maximize detection efficiency.The network’s topology is based on the quasi-honeycomb structure.Under the assumption that the network is fully connected,the algorithm maximizes the network’s static detection and joint detection capabilities.Furthermore,it can enable rapid deployment and improve network detection efficiency.The simulation results show that the proposed algorithm can significantly improve the coverage and detection efficiency of WSN.WSN can obtain more accurate and comprehensive detection area data and corresponding perception information for data fusion positioning based on the previous research content.This thesis proposes a multi-station collaborative clustering fusion positioning method to ensure positioning accuracy and improve data fusion speed by considering the relationship between network deployment nodes and the target region.Firstly,in the case of a limited number of deployed fixed nodes,the target area is divided based on detection node coverage,and different positioning schemes are used for the various divided areas.Secondly,effective data information for data fusion is extracted from the acquired target data through data association.Finally,the thesis proposes a clustering method for eliminating ghosts based on Mahalanobis distance.Furthermore,simulation results show that the algorithm can effectively achieve efficient target positioning in beyond-line-of-sight scenes and improve target detection efficiency.