Research On The Method Of Radiant Source Position Estimation Based On RS

Location-based services have grown rapidly in recent years,and electromagnetic radiation interference has become increasingly frequent,which makes wireless sensor network(WSN)localization face huge challenges.Localization based on received signal strength(RSS)has the advantages of simple data acquisition,easy operation,low cost and low hardware requirements,and has broad application prospects.To provide users with high-quality location services and alleviate the pressure of electromagnetic radiation pollution management,this paper focuses on the research of localization algorithms based on semidefinite programming(SDP)and secondorder cone programming(SOCP)from the measurement model.The main contents of this paper are summarized as follows:(1)To address the problem of how to schedule sensor node resources for efficient localization in WSN localization,an improved localization algorithm for RSS combining sensor selection and SDP is proposed.The algorithm combines the advantages of distributional features between sensors with least squared relative error(LSRE)based localization.Sensor selection is incorporated into the algorithm for localization.The algorithm uses a set of sensors to passively measure RSS to estimate an initial location.Based on the initial estimate,the geometry of the sensor network is analyzed.Sensors with a certain angular separation around the radiation source are selected to actively measure RSS and complete the secondary accurate estimation.Simulation experiments show that the proposed algorithm achieves considerable performance gains over the SOCP-based and the SDP-based methods in terms of location and transmit power estimation.(2)In the real communication environment,the electromagnetic signal is affected by multipath fading,multi-user interference and Non-Line of Sight(NLOS)channel transmission,making the measurement noise not follow a single Gaussian distribution.To address this problem,this paper uses Gaussian Mixture Model(GMM)to model the actual additive noise,and proposes an unknown parameter localization algorithm based on GMM and SDP.The algorithm constructs a GMM-based maximum likelihood(ML)objective function with unknown transmit power.By using Jensen inequality and SDP techniques,the difficult ML problem is transformed into a convex optimization problem to be solved.Simulation results show that the algorithm reduces the error in the NLOS propagation environment based on the use of RSS for radiation source localization.(3)To address the problems of complex propagation link identification and large localization errors in existing NLOS localization algorithms,an RSS-based SOCP localization algorithm is proposed under NLOS.With unknown transmit power,the algorithm introduces balancing parameters to model the NLOS localization problem as a Least Squares problem.An approximate optimal solution is found by relaxing the LS problem to a SOCP problem.Based on the estimated transmit power,a new SOCP problem is constructed and solved to achieve higher localization accuracy.Simulation results show that the proposed algorithm significantly improves the estimation accuracy of the transmit power and location under NLOS conditions with a smaller complexity overhead than existing methods.