In this study, we present a novel simultaneous localization and mapping (SLAM) system, VIMS, designed for underwater navigation. Conventional visual-inertial state estimators encounter significant practical challenges in perceptually degraded underwater environments, particularly in scale estimation and loop closing. To address these issues, we first propose leveraging a low-cost single-beam sonar to improve scale estimation. Then, VIMS integrates a high-sampling-rate magnetometer for place recognition by utilizing magnetic signatures generated by an economical magnetic field coil. Building on this, a hierarchical scheme is developed for visual-magnetic place recognition, enabling robust loop closure. Furthermore, VIMS achieves a balance between local feature tracking and descriptor-based loop closing, avoiding additional computational burden on the front end. Experimental results highlight the efficacy of the proposed VIMS, demonstrating significant improvements in both the robustness and accuracy of state estimation within underwater environments.

Using the Global Positioning System (GPS) and the mobility of marine surface vehicles, this paper addresses the navigation problem between unmanned surface vehicles (USVs) and autonomous underwater vehicles (AUVs). We propose a moving AUV state estimation method based on the trajectory optimization of the USV. In particular, by exploring the Doppler effect on the frequency of arrival (FOA) of the acoustic signals received by a single-surface USV, the position and velocity of the AUV can be estimated simultaneously, offering a robust solution that eliminates the need for time synchronization. Moreover, the USV trajectory is dynamically adjusted to achieve optimal USV–AUV measurement geometry, thereby improving the AUV’s observability and enhancing state estimation performance. The innovation lies in a tailored cost function grounded in observability analysis via the Cramér–Rao lower bound (CRLB) and geometric constraints. It integrates (1) the CRLB to optimize system observability, thereby enhancing estimation accuracy, (2) a distance term to ensure that the USV maintains appropriate proximity to the AUV, and (3) a turning rate term that adjusts the USV’s orientation to improve following capability. The cost function is then minimized using a particle swarm optimization algorithm, balancing these components to achieve a robust AUV tracking framework. We conduct comprehensive simulations to examine the potential influences of different factors, including the complexity of the USV trajectory, AUV depth, measurement frequency, packet loss rate, and noise levels, on navigation performance. Simulation results demonstrate the effectiveness of the proposed method in estimating and tracking the AUV.

Autonomous underwater vehicles (AUVs) are widely used in sampling on-site the seawater parameters, such as temperature, salinity and biomass for better understanding the ocean. The AUV path needs to be carefully planned in order to maximize the sampled information within the power constraints, which is known as the informative path planning (IPP). The existence of ocean currents further complicates the problem. This article proposes an IPP method for AUVs under the influence of ocean currents via combining the probabilistic roadmap and$Q$-learning. Specifically, the$Q$-learning algorithm builds an informative optimal AUV path by traversing a learned and updated$Q$-table. The$Q$-value in the table represents the expectation of the obtained reward if taking a certain action moving from one position to another. Considering the characteristics of the IPP task, we design the reward matrix in$Q$-learning using the prior knowledge on the environment information. A convergent$Q$-table guarantees that only one complete training and learning is required to generate the path between any two positions. This feature facilitates converting the possible repetitive path plannings into simple search problems, and thus the automatic return is easily realized whenever the AUV residual energy is insufficient. Moreover, to improve the efficiency of the$Q$-learning algorithm, a probabilistic roadmap with random sampling is generated and combined with the$Q$-learning. Various simulations and comparisons are carried out. The results demonstrate the effectiveness of the proposed IPP algorithm, showing that the convergence of the path planning can be achieved quickly and successfully. The superiority in terms of efficient return path planning over the traditional path planning method, RRT*, is also demonstrated.

In the underwater medium, the speed of sound varies with water depth, temperature, and salinity. The inhomogeneity of water leads to bending of sound rays, making the existing localization algorithms based on straight-line propagation less precise. To realize high-precision node positioning in underwater acoustic sensor networks (UASNs), a multi-layer isogradient sound speed profile (SSP) model is developed using the linear segmentation approximation approach. Then, the sound ray tracking problem is converted into a polynomial root-searching problem. Based on the derived gradient of the signal’s Doppler shift at the sensor node, a novel underwater node localization algorithm is proposed using both the time difference of arrival (TDOA) and frequency difference of arrival (FDOA). Simulations are implemented to illustrate the effectiveness of the proposed algorithm. Compared with the traditional straight-line propagation method, the proposed algorithm can effectively handle the sound ray bending phenomenon. Estimation accuracy with different SSP modeling errors is also investigated. Overall, accurate and reliable node localization can be achieved.

The demand for transport between terminals within port areas, known as inter terminal transportation (ITT), is increasing. This paper proposes a dynamic rolling horizon scheduling strategy for ITT using a fleet of waterborne Autonomous Guided Vessels (waterborne AGVs). The strategy is dynamic in that it can handle dynamically arriving ITT requests. Every certain period of time, transport schedules are updated according to the current vessel states, dynamic waterway transport network, and ITT requests over a future time horizon. Specifically, the dynamic scheduling problem is mathematically modeled in a rolling horizon fashion considering time windows of ITT requests, capacity limits of waterborne AGVs and load/unload service times at terminals. Considering the computational complexity for possible large scale ITT scenarios, we further propose an efficient solution approach based on improved insertion, tabu search and restart heuristics. Initial routes are first constructed by inserting new ITT requests into the previously computed routes in the rolling horizon framework. Tabu search with two types of neighborhoods are then designed to improve the initial routes. Moreover, a select-remove-insert restart procedure is activated to diversify the search space whenever necessary. A waterborne ITT network in the port of Rotterdam is considered. Comprehensive simulations based on realistic ITT dataset are run to demonstrate the effectiveness of the proposed dynamic scheduling strategy. This work could be readily used to build towards a fully autonomous waterborne ITT system. Insights that support long term strategical decisions, such as the fleet size, could also be gained from the simulations.

Underwater docking greatly facilitates and extends operation of an autonomous underwater vehicle (AUV) without the support of a surface vessel. Robust and accurate control is critically important for docking an AUV into a small underwater funnel-type dock station. In this paper, a docking system with an under-actuated AUV is presented, with special attention paid to control algorithm design and implementation. For an under-actuated AUV, the cross-track error can be controlled only via vehicle heading modulation, so both the cross-track error and heading error have to be constrained to achieve successful docking operations, while the control problem can be even more complicated in practical scenarios with the presence of unknown ocean currents. To cope with the above issues, a control scheme of a three-hierarchy structure of control loops is developed, which has been embedded with online current estimator/compensator and effective control parameter tuning. The current estimator can evaluate both horizontal and vertical current velocity components, based only on the measurement of AUV’s velocity relative to the ground; in contrast, most existing methods use the measurements of both AUV’s velocities respectively relative to the ground and the water column. In addition to numerical simulation, the proposed docking scheme is fully implemented in a prototype AUV using MOOS-IvP architecture. Simulation results show that the current estimator/compensator works well even in the presence of lateral current disturbance. Finally, a series of sea trials are conducted to validate the current estimator/compensator and the whole docking system. The sea trial results show that our control methods can drive the AUV into the dock station effectively and robustly.

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With the spatial-temporal focusing of acoustic energy, time reversal processing (TRP) shows the potential application for active target detection in shallow water. To turn the ‘potential’ into a reality, the TRP based on a model source (MS) instead of a physical probe source (PS) is investigated. For uncertain ocean environments, the robustness of TRP is discussed for the narrowband and broadband signal respectively. The channel transfer function matrix is first constructed in the acoustic perturbation space. Then a steering vector for time reversal transmission is obtained by singular value decomposition (SVD) of the matrix. For verification of the robust TRP, the tank experiments of time reversal transmission focusing and its application for active target detection are undertaken. The experimental results have shown that the robust TRP can effectively detect and locate a small bottom target.

Nonlinear parameter estimation often displays a threshold phenomenon, that is, below certain signal-to-noise ratio (SNR) the estimation mean-square error (MSE) increases dramatically. The method of interval errors (MIE) has been shown to provide accurate MSE prediction of related nonlinear techniques well into the estimation threshold region, yet relatively simple and robust in evaluation compared to a global performance bound. However those features have not been understood on a strict theoretical basis. This paper investigates numerical sensitivity of the MIE to parameter sampling resolution, aiming to understanding, from information theory perspective, the underlying mechanism leading to robust MSE approximation. A recently-developed information theory resolution bound is reinterpreted and applied to specify the parameter sampling resolution. Numerical evaluation of the relevant results for array-based bearing estimation supports the proposed connection between the resolution bound and the MIE.

Array-based bearing estimation often displays a threshold behavior, that is, below certain signal-to-noise ratio (SNR) the estimation mean-square error (MSE) increases dramatically. The error increase is known to be largely attributed to sidelobe ambiguities in signal field correlation along with estimation bias at low SNR. This paper investigates the bias-related contribution from the perspective of local performance bounds. The first-order bias of the maximum likelihood estimate is first derived for a complex multivariate Gaussian data model, which is then incorporated into the Cramer-Rao bound and Barankin bound respectively to obtain MSE approximations. The simulations show an improved threshold region error prediction compared to the same bounds without bias correction.

In typical parameter estimation problems, the signal observation is a function of the parameter set to be estimated as well as some background (environmental/system) parameters assumed known. The assumed background could differ from the true one, leading to biased estimates even at high signal-to-noise ratio (SNR). To analyze this background mismatch problem, a Ziv-Zakai-type lower bound on the mean-square error (MSE) is developed based on the mismatched likelihood ratio test (MLRT). At high SNR, the bound incorporates the increase in MSE due to estimation bias; at low SNR, it includes the threshold effect due to estimation ambiguity. The kernel of the bound's evaluation is the error probability associated with the MLRT. A closed-form expression for this error probability is derived under a random signal model typical of the bearing estimation/passive source localization problem. The mismatch is then analyzed in terms of the related ambiguity functions. Examples of bearing estimation with system (array shape) mismatch demonstrate that the developed bound describes the simulations of the maximum-likelihood estimate well, including the sidelobe-introduced threshold behavior and the bias at high SNR.

This paper presents a general framework for the application of wavelets in array signal processing, including an understanding of the scale. A multiresolution-signal direction-of-arrival (DOA) estimation scheme is proposed based on the wavelet decomposition. Simulations show that the new approach improves the DOA estimation performance, particularly for sources near the broadside direction.