The Global Navigation Satellite System combined with acoustic technique has achieved great economic benefits in positioning of ocean bottom seismometers, with hundreds of underwater transponders attached to seismometers typically being deployed during oil exploration. The previous single transponder positioning method ignored the similar underwater environments between the transponders. Due to the refraction effect of sound, the technique usually showed poor positioning accuracy in shallow water when the incidence angles are large. In this paper, the effect of sound ray bending is analyzed based on the sound ray tracing method in shallow water, and a new piecewise incidence angle model is proposed to improve the positioning accuracy of multiple objects in order to estimate the sound ray bending correction. The parameters of the new model are divided into groups and estimated by sequential least squares method, together with all of the transponders. The observability analysis is discussed in simulation and testing experiments in the South China Sea. The results show that the newly proposed method is able to make full use of the acoustic observation data of hundreds of transponders to accurately estimate the SRB correction, which could also significantly improve the positioning accuracy of multiple transponders.

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.

The autonomous and remotely vehicles (ARV) plays a crucial role in deep-sea and large-scale surveying and sampling missions, which requires accurate marine spatial positioning and navigation information. Generally, the integration of ultra-short baseline (USBL) acoustic positioning system, strap-down inertial navigation system (SINS) and Doppler velocity logger (DVL) is consistently recommended for underwater geo-referencing. This inertial-acoustic fusion offers superior accuracy and reliability in determining vehicle position, speed, and attitude. However, the temporal variation of sound speed structure (SSS) emerges as a critical factor that limits the USBL positioning performance within operational areas, thereby compromising the fusion accuracy of integrated navigation systems. In this contribution, we propose a novel in-situ sound speed structure correction scheme for SINS/USBL integrated navigation. Firstly, the temporal variation of SSS based on USBL positioning functional model is analyzed, and a two-dimensional temporal SSS model is constructed. Subsequently, a novel adaptive two-stage information filter algorithm is proposed, which estimates the perturbation of the sound speed while simultaneously detecting and identifying outlier observations from both the USBL and DVL. Finally, the effectiveness of the improved method is verified through simulations and trails conducted in the South China Sea. The experimental results demonstrate that the proposed method effectively estimates the sound speed disturbance in real-time, significantly enhancing the performance of tightly integrated inertial-acoustic navigation systems.Keywords SINS/USBL, sound speed structure, adaptive two-stage information filter, integrated navigation