Research On Joint Location Algorithm For Wireless Sensor Networks

In recent years,with the rapid development of Internet technology and intelligent devices such as mobile phones and smart watches,the information connection between things is more common.It makes wireless sensor networks(WSNs)an important research field.As a support technology in wireless sensor networks,its geographic location information service has been widely concerned in many fields,such as the military field and civil field.At present,the satellite navigation system can provide high-precision positioning and navigation services for users.However,it cannot effectively provide localization information in the complex environments,e.g.,densely built cities,forests,indoor.Therefore,positioning technology based on wireless sensor networks has been extensively studied and developed rapidly.The WSNs localization technology can estimate the target node's specific location according to the noise measurement information of the sensor node and the related algorithm on premise knowing the location of some sensor nodes in the network.According to different physical measurement methods,it can be divided into time-ofarrival(TOA),time-difference-of-arrival(TDOA),received signal strength(RSS),and angle-ofarrival(AOA)and their joint positioning methods,etc.,researchers have conducted a lot of related research in this field.Due to the common positioning method,a variety of useful information can be measured,and the positioning accuracy is higher.In this paper,we mainly study the joint positioning algorithm in WSNs based on mixed measurement information.Specifically,the main contributions of this paper are summarized as follows:(1)For localization problem based on the joint TOA and AOA in 3D space.Firstly,a localization model is established based on the distance and angle measurement information.A new objective function is established based on the weighted least squares criterion to solve the joint localization problem.Specifically,a generalized trust-region subproblem framework is proposed to approximate the non-convex maximum likelihood estimation problem,which can be solved quickly and accurately by dichotomy.In addition,the convex relaxation technique is further used to transform the joint positioning problem's objective function into a second-order cone programming problem(SOCP),which is solved by the interior point method.The experimental results show that the two methods' performance is better than that of the existing methods,and is closer to the Cramer Rao lower bound,which achieves better positioning performance.(2)For the non-line of sight propagation problem caused by many obstacles in indoor and other complex environments.We propose a standard localization method based on joint RSS and TOA in the non-line of sight environment.Assuming that the non-line-of-sight errors follow a uniform distribution,the problem of minimizing the objective position is established according to the power and distance measurement model,and the least-squares criterion.The convex relaxation technique is used to transform the problem into a SOCP programming problem,which can be solved effectively.The experimental results show that the proposed method has high positioning accuracy and can effectively suppress non-line of sight error.