Orthogonal Regression Based Multihop Localization Algorithm for Large-Scale Underwater Wireless Sensor Networks

Abstract

For large-scale underwater wireless sensor networks (UWSNs) with a minority of anchor nodes, multihop localization is a popular scheme for determining the geographical positions of normal nodes. However, existing multihop localization studies have considered the anchor positions to be free of errors, which is not a valid assumption in practice. In this paper, the problems existing in nonlinear least square-based node self-localization schemes are analyzed, and the biased distribution characteristic of multihop distance estimation errors is pointed out. Then, the orthogonal regression method is employed for the localization of normal nodes in the presence of anchor position errors. In particular, the influences of errors in independent variables and biases in dependent variables on node coordinate estimation are taken into account simultaneously. Extensive simulation results illustrate the robustness and effectiveness of our method.