LS-SVM Based WSN Location Algorithm in NLOS Environments

Abstract

Location methods based on learning theory perform well in wireless cellular networks. These methods may be further improved since range measurements among all nodes are not taken into consideration, while these range measurements in the WSN location system are generally available. In this paper, we propose an improved LS-SVM based location algorithm to solve mobile location problem in a NLOS environment. We extend LS-SVM method from wireless cellular networks to WSN location system. Compared with LSSVM in wireless cellular networks only using the range measurements between anchor nodes and blind nodes, the proposed method can improve the positioning accuracy by using all the range measurements among the nodes. Moreover, steepest descent method is used in the proposed method to iterative search the optimal position estimation of blind nodes. The simulations results in different cases illustrate that the proposed algorithm outperforms the kernel method and LS-SVM method on location accuracy.