Sparse Feature Points Extraction-Based Localization With Partial Information Loss in UWSNs

This article investigates the problem of localization of mobile nodes in the marine environment under conditions of information deficiency. To solve this problem, sparse feature points (SFPs) extraction-based time-of-flight (ToF) minimum residual localization algorithm and localization based on the prior information of the target under partial information loss are proposed. First, the SFPs’ extraction method is adopted to fit the sound speed profile (SSP). Based on this, the SFPs’ extraction-based ToF computational model is proposed. The coordinates of the nodes to be located are calculated by ToF measurement and the ToF model for minimum residual localization. Second, in the case of information loss, particle ranges are generated using the target history a prior information. This is combined with the received node localization information to locate the node by the proposed target prior-information-based localization method. Finally, the results of the simulation experiments show that the proposed method achieves a more detailed description of the SSP characteristics. The localization error of the proposed method is reduced fivefold compared with other methods under the condition of information loss, which is more in line with the spatial characteristics of the underwater environment.