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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.

Distributed locating algorithm MDS-MAP (LF) based on low-frequency signal