The development of underwater high-precision navigation technology is of great significance for the application of autonomous underwater vehicles (AUVs). Traditional long baseline (LBL) positioning systems require pre-deployment and the calibration of multiple beacons, which consumes valuable time and manpower. In contrast, the range-only single-beacon (ROSB) positioning technology can help autonomous underwater vehicles (AUVs) obtain accurate position information by deploying only one beacon. This method greatly reduces the time and workload of deploying beacons, showing high application potential and cost ratio. Given the operational constraints of AUV open-ocean navigation with single-beacon weak observations and absence of valid a priori positioning data in calibration zones, a multi-sensor underwater virtual beacon localization framework was established, proposing a differential Chan–Gauss–Newton (DCGN) methodology for submerged vehicles. Based on inertial navigation, the method uses the distance measurement information from a single beacon and observations from multiple sensors, such as the Doppler velocity log (DVL) and pressure sensor, to obtain accurate position estimates by discriminating the initial position of multiple hypotheses. A simulation experiment and lake test show that the proposed method not only significantly improves the positioning accuracy and convergence speed, but also shows high reliability.

Underwater acoustic single-beacon positioning technology achieves localization by integrating vehicle motion with range measurements acquired from acoustic ranging devices, offering advantages such as system simplicity, flexible deployment, and high cost-effectiveness. However, its accuracy is limited by weak initial observability and degraded observation geometry. To address this, a sensor data correction and collaborative optimization framework is proposed. A hybrid outlier rejection strategy first suppresses acoustic multipath and sensor noise. To compensate for systematic sensor errors ignored in conventional Virtual Long Baseline methods, an affine transformation maps the true trajectory to the sensor-indicated one, reformulating error compensation as a correction to virtual beacon coordinates. To further mitigate the accuracy degradation caused by degenerated geometric configurations, this paper proposes a collaborative algorithm that integrates Chan initialization with affine transformation optimization. This approach formulates the positioning problem as an optimization task, simultaneously estimating the position information and affine transformation parameters through iterative refinement to achieve high-precision localization. The process begins with Chan’s algorithm, which provides an initial estimate from the virtual sensor array. This estimate is then refined under affine constraints to achieve high-precision localization. Experimental results show the method improves positioning accuracy by 36.30% compared to baseline algorithms, demonstrating significant performance enhancement.

To address the inherent limitations of traditional multi-beacon hydroacoustic positioning systems (e.g. ultra-short base line or long base line systems), such as low cost-effectiveness and poor reliability, this paper investigates a low-cost underwater navigation method based on a single beacon. Most existing single hydroacoustic sensor navigation studies rely on filtering models derived from kinematic dead reckoning, which suffer from excessive approximations and limited accuracy. Among the limited number of studies based on inertial navigation error models, the linearized approximation method is still commonly used to handle nonlinear measurement equations. The main contributions of this work are twofold: First, based on the inertial navigation error equations, a systematic integrated filtering model combining single hydroacoustic sensor ranging with the strapdown inertial navigation system is established, providing a more complete theoretical framework. Second, a nonlinear filtering architecture based on the Unscented Kalman Filter is proposed. By developing a complete measurement equation system, the linearization approximation error is fundamentally avoided. Sea trial results demonstrate that the proposed method ensures navigation accuracy while significantly reducing system costs, offering a cost-effective navigation solution for autonomous underwater vehicles.