Robust Received Signal Strength Based Localization In Indoor Environments

In recent years,the development of technological is getting faster and faster and the importance of information acquisition is increasing.Wireless Sensor Networks(WSN)get the rapid development because of providing information collection services for many application fields,the most important is the target location information.At present,there are many basic methods for achieving target positioning.The most widely used is positioning methods based on time information.However,the localization based on Received Signal Strength(RSS)is the most suitable method in some restricted environments because of its low complexity.However,when the positioning environment is indoors,many obstacles,such as walls,will cause a Non-Line-of-Sight(NLOS)transmission path between the source and the sensor node.The additional path loss caused by NLOS seriously affect the performance of the positioning method.In addition,in the traditional positioning method based on the received signal strength,the parameters need to be estimated during the calibration phase,which increases the communication burden.In view of the above difficulties,the main study results of this paper are as follows:1.Under the case of the known model parameters,for the impact of additional path loss on positioning methods,we propose a robust weighted least square(RWLS)method in this letter.Then we introduce a balancing parameter about the additional path losses and formulate a minimax optimization problem to estimate the source location,simultaneously,against the additional pathloss terms.We obtain a non-convex minimization problem by eliminating the “max” part with SLemma.At last,the Semidefinite Relaxation(SDR)is used to obtain a tractable convex Semidefinite Programming(SDP).Simulation results show that the proposed method works better than several existing methods in most environments and works well in a time varying environment.2.Under the case of the unknown model parameters,we propose an alternating estimation procedure based on RWLS method to jointly estimate the model parameters and the source location,which can reduce the complexity of the algorithm.Simulation results show that the proposed method works better than several existing methods in most environments and works well in a time varying environment.