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There are two important aspects in optimally processing the acoustic measurements: the establishment of the functional model and the refinement of the associated stochastic model. In most cases the functional model may be sufficiently well-established, but the stochastic model error has not received considerable attention and it still remains a challenging research topic in real-time positioning. The equal weight stochastic model (EWSM) commonly used at present is under the assumption of equal-precision observation, which is imprecise and not coincident with reality. The purpose of this paper is to develop an approach to obtaining more accurate weights of observables by real-time estimating the variance of observations. The inaccurate stochastic model effect is analyzed and the real time stochastic model (RTSM) estimated by using the least squares residual of observations is presented to improve the positioning accuracy. Simulation results have shown that the underwater positioning accuracy goes from 0.173 m to 0.157 m based on GPS Intelligent Buoy (GIB) by employing the real time stochastic model, while that can be improved from 0.426 m to 0.306 m based on surveying vessel (SV) when adopting RTSM. Both simulations based on GIB and SV indicate that real-time stochastic model can perform better than conventional equal weight stochastic model in positioning accuracy.

Underwater Towed Vehicle navigation and localization in deep sea environments are particularly challenging without high-precision inertial sensors. In this paper, the multi-sensors navigation fusion method of a towed underwater vehicle based on the ultrashort baseline (USBL) system, Doppler velocity log (DVL) and a pressure gauge in the deep sea is investigated. Combined with GPS real-time tide detection technology, the positioning principle of the USBL based on the depth constraint is given, and a straight-line model be proposed to estimate the position of the vehicle used the average heading when underwater towed vehicle is working normally in deep sea without DVL. The interacting multiple model (IMM) algorithm based on smoothing algorithm is proposed to improve the positioning accuracy in the deep sea, and the nonlinearity of the model, precision and stability of filtering are considered simultaneously using the adaptive robust square root cubature Kalman filter. The results of the simulated and practical experiments at 4800 m verify that the new algorithm can take full use of high-precision observation information and greatly improve the positioning accuracy and robustness of a deep sea underwater towed system.