Autonomous navigation of underwater vehicles in infrastructure-limited environments presents persistent challenges due to the constraints of traditional acoustic positioning systems. Sparse long baseline (sparse LBL) navigation, which relies on a minimal set of acoustic transponders, offers a promising alternative but suffers from geometric ambiguity and reduced robustness without external aiding. This paper introduces an integrated approach to measurement-based navigation and control in the sparse LBL setting with two base transponders, focusing on three key components. First, a novel three-stage navigation algorithm is proposed, which enables unambiguous robust leader–follower formation position estimation using only two acoustic transponders and onboard measurements. Second, a hybrid state estimation framework is developed to fuse asynchronous data from inertial sensors, depth measurements, and acoustic ranging, accommodating measurement uncertainty and timing variability. Third, there is a nonlinear trajectory tracking controller based on state-dependent coefficients (SDCs) technique. The combined approach enables accurate and robust leader–follower structure navigation with minimal acoustic infrastructure and is suitable for deployment in dynamic or remote underwater scenarios. The numerical simulations demonstrate the acceptable motion control accuracy.
Kabanov et al. (Thu,) studied this question.