A Study On Joint Localization Algorithms Based On Convex Optimization In WSNs

Due to the important applications of Wireless Sensor Networks(WSNs)in the fields of target tracking,Internet of Things technology and wireless communications,accurate localization algorithms for WSNs have received considerable attention over the past years.Especially for joint localization algorithm,which can take advantage of a variety of different location information,is able to meet the requirements of high-precision positioning.Joint TDOA and AOA localization has been widely used in present because the low requirements for synchronous performance.In this paper,joint TDOA-AOA localization algorithm is studied with mathematical analysis and simulation experiment,which is mainly realized by the following aspects:Owing to non-linearity and non-convexity of the relationship between measurements and unknown sensor,joint localization results may converge to local optimum solution.A lot of joint TDOA-AOA localization methods are discussed in this paper.At the same time,the shortcomings of the classical algorithms are also pointed out.After that,an improved joint TDOA-AOA method is proposed by combining the reconstructed joint localization algorithm with semidefinite programming.Experimental results demonstrate that the proposed approach has higher accuracy compared with the existing algorithms.What's more,the proposed SDP approach still works well with only two anchor nodes online.Meanwhile,joint TDOA-AOA localization problem in the presence of sensor location errors is considered in this paper.By considering the uncertainty of sensor locations and analyzing the error terms in the localization problem,the influence factor is deduced and incorporated into the weighted matrix.Finally,a new SDP method for joint TDOA-AOA localization with uncertainty anchor nodes position is proposed.By compare the results of simulation,the localization performance has significant improvement after handling the sensor position errors.