Distributed Locating Algorithm MDS-MAP (LF) Based on Low-Frequency Signal

Chunyue Zhou, Tong Xu, Hairong Dong

The positioning error of distributed MDS-MAP algorithms comes from two aspects: the local positioning error and the position fusion error. In an attempt to improve the positioning result in both local positioning accuracy and global convergence probability, this paper proposes a novel MDS-MAP(LF) algorithm, which uses low frequency signal to measure the inter-sensor distance rather than shortest path algorithms. The proposed MDS-MAP(LF) algorithm leverages the propagation feature of low frequency signal to acquire a more precisely two-hop distance. The simulation and analysis results indicate that the accuracy of local positioning is improved by more than 3%. With the use of cluster expansion, MDS-MAP(LF) also shows a better convergence with comparison to the former classical distributed MDS-MAP algorithm.