Non-line-of-sight Localization Algorithm For Wireless Sensor Network

In recent years,location information has gained widespread attention in a number of areas including unmanned driving,environmental monitoring,smart home,and military reconnaissance.At present,Global Positioning System(GPS)and Bei Dou Navigation System(BDS)are widely used in wireless localization.The satellite systems provide users around the world with all-weather,alltime,high-precision localization and navigation services,and also provide a direction for the development of wireless localization.The satellite systems can obtain high-precision location information in outdoor environments.However,in some densely constructed cities,indoors,underground,and other complex environments,the signals from satellite are weak and even unable to be received.The demand for indoor localization has greatly promoted the development of wireless sensor networks(Wireless Sensor Networks,WSNs).Depending on the characteristic parameters of the received signal and the application environment,wireless localization methods include Time-ofArrival(TOA),Time-Difference-of-Arrival(TDOA),Angle-of-Arrival(AOA),Received-SignalStrength(RSS),and hybrid localization of various localization technologies.However,in actual measurement,the localization problem is usually non-convex and non-linear,and the statistical information of the signal is usually not available in complex environments.In response to these problems,the work and contributions completed in this thesis mainly include the following two aspects:1.A novel time-of-arrival–based localization algorithm in mixed line-of-sight/non-line-ofsight environments is proposed.First,an optimization problem of target localization in the known distribution of line-of-sight and non-line-of-sight is established,and mixed semi-definite and second-order cone programming techniques are used to transform the original problem into a convex optimization problem which can be solved efficiently.Second,a worst-case robust least squares criterion is used to form an optimization problem of target localization in unknown distribution of line-of-sight and non-line-of-sight,where all links are treated as non-line-of-sight links.This problem is also solved using the similar techniques used in the known distribution of line-of-sight and non-line-of-sight case.Finally,computer simulation results show that the proposed algorithms have better performance in both the known distribution and the unknown distribution of line-of-sight and non-line-of-sight environments.2 Aiming at the problem of lack of signal statistical information,a method of Back Propagation(BP)neural network localization in non-line-of-sight environment is studied.First,the method of using BP neural network to directly obtain localization information is discussed.The distance measurement and node coordinate values are used to train and test the neural network,and the neural network localization algorithm model is established.The algorithm doesn't need to know any statistical information of non-line-of-sight errors.It only needs sample data of distance measurement values and node coordinate values,but it is easy to fall into the local optimum and requires many sampling points.Second,a localization method combining BP neural network and least square method is proposed for this problem.The distance measurement and distance error are used to train and test the neural network,and a new neural network algorithm model is established.According to the measurement distance error,the measurement data including non-line-of-sight error is corrected.The licalization of the target node is obtained by using the least square method.Finally,computer simulations show that the method of combining BP neural network with LS has good stability in non-line-of-sight environment.