Abstract
As the mobile node localization algorithm in three-dimensional environment cannot meet the demand of actual application, a hybrid mobile node localization algorithm for a wireless sensor network (WSN) in three-dimensional environment is proposed in this paper, which is based on the least squares support vector regression (LSSVR) and Kalman filter (KF). The proposed algorithm firstly constructs the LSSVR localization model by sampling measurement area and training sample sets. Then, the KF model is used to iterate and correct the measured distance in order to obtain the distance between the unknown node and each anchor node. Finally, the LSSVR localization model is employed to obtain the estimated location of the unknown node. The experiments were conducted and the experimental results were analyzed according to ranging errors, anchor node density, communication radius, moving speed, and node localization errors. Simulation results show that the proposed algorithm using a joint KF and LSSVR algorithm is superior to the KF algorithm and the LSSVR algorithm, and it can reduce the localization errors and improve the localization accuracy.
Highlights
In a wireless sensor network (WSN), a large number of applications require the information on geographical location of the node within the network in order to get the accurate location of the information source
We propose a method based on least squares support vector regression (LSSVR) and Kalman filter (KF) algorithm to take advantage of both techniques to locate mobile nodes in three-dimensional environment
6 Conclusions According to the mobile node characteristics in a complex environment, a three-dimensional localization algorithm for mobile node based on the KF-LSSVR algorithm, is proposed
Summary
In a wireless sensor network (WSN), a large number of applications require the information on geographical location of the node within the network in order to get the accurate location of the information source. We propose a method based on LSSVR and KF algorithm to take advantage of both techniques to locate mobile nodes in three-dimensional environment. In this algorithm, KF algorithm was used to filter and estimate the position, and LSSVR algorithm was used to model and locate. X0l , y0l , and z0l are the actual location coordinates of the virtual sampling point S0l in the detection area, Vl is the distance vector from the sampling point to the anchor node, and fx, fy, fz are the estimated values of the regression model established by the optimization model parameter. Step 8: use the distance vector obtained in the seventh step to obtain the estimated location of the unknown nodes
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