Aiming at error and robustness of localization algorithms in underwater acoustic sensor networks

we proposed two monte carlo mobile localization algorithms: Circular ring monte carlo localization (CRMCL) and particle swarm optimization for circular ring monte carlo localization (PRMCL). CRMCL used one-hop anchor nodes to construct circular sampling area and ring filter. By defining sampling points density

we obtained reasonable sample number. The relationship between the ring parameter and the filtration area was demonstrated. Then the reasonable ring parameter was obtained through the simulation experiments

and an efficient filter was constructed to reduce the localization error. PRMCL used particle swarm optimization (PSO) algorithm to optimize the samples filtered by CRMCL

which reduced the number of invalid samples and improved the robustness of localization. The simulation results show that CRMCL and PRMCL have lower localization error and better robustness than monte carlo localization and other improved algorithms without additional hardware.