Underwater Long-Term Terrain-Aided Navigation System Considering the Matching Result Confidence Assessment

The long-term navigation capability of Underwater Internet of Things (UIoT) mobile nodes is essential for building the smart ocean. Currently, the terrain-aided navigation (TAN) method using a single-beam echo sounder (SBES) has shown its potential for achieving long-term underwater navigation, but the robustness of the SBES-TAN positioning results is hindered by the limited measurement information of the SBES. Incorporating terrain matching result confidence assessment into the SBES-TAN method is an effective way to enhance its application and improve the accuracy of navigation results. This article proposed a Markov process-based matching result confidence assessment (MPMRCA) method for the SBES-TAN system. To address the accumulated positioning error in the filter theory, both the positioning error and its variation are considered in the matching result confidence assessment. Additionally, a two-stage confidence assessment strategy is introduced to improve the accuracy of the assessment. Finally, an SBES-TAN method incorporating the MPMRCA is proposed to improve the robustness of navigation results. The validity of the SBES-TAN method, enhanced by the MPMRCA, is verified using experimental data from an autonomous underwater vehicle in a lake environment. The results demonstrate that the proposed method can effectively provide robust and accurate navigation results, benefiting long-term underwater missions for UIoT mobile nodes.