Investigation on Stochastic Model Refinement for Precise Underwater Positioning

Abstract

More accurate and realistic stochastic model is required for high-precision underwater positioning. The common stochastic modeling procedure, assuming that the measurements are statistically independent in space and time and have same accuracy, is certainly not realistic. Any misspecification in the stochastic model may have a significant effect on underwater acoustic data processing. Taking the heteroscedastic space and time correlation into consideration, a novel stochastic modeling procedure based on an iterative minimum norm quadratic unbiased estimator method has been developed to improve underwater positioning accuracy. Testing results by experiment data show that the positioning accuracy can be superiorly improved from 1.226, 1.434, and 1.018 m based on a traditional stochastic model estimation scheme to 0.935, 0.336, and 0.190 m for three transponders when adopting the new method.