Kalman filter based acoustic positioning of deep seafloor datum point with two-step systematic error estimation

The systematic errors caused by temporal spatial variation of sound speed and sonar signal delays significantly reduce the positioning accuracy of deep seafloor datum points. To overcome this problem, a Kalman filter (KF) based on two-step systematic error estimation is proposed for the acoustic positioning of deep seafloor datum points. The proposed algorithm first calculates the equivalent sound speed by sound ray tracing. A KF based on first-step systematic error estimation is then constructed to calculate the parameters of position and first-step systematic error, and to obtain the slant range residuals. Based on the slant range residuals, the systematic error related to long-period errors of sound speed is parameterized by empirical mode decomposition (EMD) and function fitting. Finally, a KF based on second-step systematic error estimation is used to calculate the parameters of position and second-step systematic error. The proposed algorithm is verified by a real experiment for the acoustic positioning of deep seafloor datum points. The results demonstrate that the proposed algorithm significantly improves the three-dimensional positioning accuracy of deep seafloor datum points compared with the least squares (LS) traditional underwater positioning model, which does not consider the influence of systematic errors, and a KF based on the single systematic error estimation. Using the proposed method, the root mean square (RMS) of the slant range residuals for deep seafloor datum points can be better than 13 cm.