Autonomous Underwater Vehicle Localization Research Articles

Underwater Localization of AUVs in Motion Using Two-Way Travel Time Measurements With Unknown Sound Velocity

For an autonomous underwater vehicle (AUV) underwater localization system based on acoustic two-way travel time measurements, the motion of the AUV during the interrogation-reception time interval, unknown underwater sound velocity, and beacon calibration error affect the performance of the positioning solution. Ignoring these factors significantly deteriorates the performance of the localization system. This paper focuses on the localization problem of AUV in motion with an unknown sound velocity and beacon calibration error. The Cramér–Rao lower bound of the concerned localization problem is first derived as the performance bound of an unbiased estimator. Next, through multi-stage processing, we design a closed-form solution that can attain the corresponding performance bound under small measurement noise conditions. Furthermore, considering that the AUV motion velocity is typically much lower than the sound velocity in practical applications, a simpler and more efficient simplified closed-form solution is proposed, which can guarantee localization performance under the condition that the AUV motion speed is low, as well as small measurement noise. Finally, the theoretical performance and superiority of the proposed solutions were verified through numerical simulation.

Drift Reduction in Underwater Egomotion Computation by Axial Camera Modeling

Localization of autonomous underwater vehicles is essential for underwater exploration, environmental monitoring, and infrastructure inspections, as only a few examples. As wirelessly transmitted data cannot be used in underwater navigation, and equivalent sensors are costly and not readily deployable, the use of cameras as a localization aid offers an attractive alternative. Nonetheless, the utilization of cameras for underwater navigation is hindered by medium-induced visual deterioration and refraction of the incident ray. While most research has been focusing on addressing the radiometric deterioration effects, we demonstrate that the introduction of a physically aware model of the refraction effect can significantly improve the platform localization. Specifically, we develop a refraction-aware continuous formulation of the egomotion model, which computes the translational and rotational velocities using optical flow measurements. We also demonstrate that a linear model can be reached despite the nonlinear ray's path. Results show an improved navigation solution echoed by a significant reduction in the drift with more than tenfold improvement in the estimated position and angular quantities compared to state-of-the-art methods. They demonstrate the benefit of exact modeling in underwater navigation.

Current-Aided Multiple-AUV Cooperative Localization and Target Tracking in Anchor-Free Environments

In anchor-free environments, where no devices with known positions are available, the error growth of autonomous underwater vehicle (AUV) localization and target tracking is unbounded due to the lack of references and the accumulated errors in inertial measurements. This paper aims to improve the localization and tracking accuracy by involving current information as extra references. We first integrate current measurements and maps with belief propagation and design a distributed current-aided message-passing scheme that theoretically solves the localization and tracking problems. Based on this scheme, we propose particle-based cooperative localization and target tracking algorithms, named CaCL and CaTT, respectively. In AUV localization, CaCL uses the current measurements to correct the predicted and transmitted position information and alleviates the impact of the accumulated errors in inertial measurements. With target tracking, the current maps are applied in CaTT to modify the position prediction of the target which is calculated through historical estimates. The effectiveness and robustness of the proposed methods are validated through various simulations by comparisons with alternative methods under different trajectories and current conditions.

An Integrated Navigation Method for Small-Sized AUV in Shallow-Sea Applications

The navigation and localization of Autonomous Underwater Vehicle (AUV) face many challenges. In surface missions, AUV typically relies only on Global Positioning System (GPS) to perform navigation. However, the GPS data output frequency is low, usually only 1 Hz. Meanwhile, the navigation system is susceptible to interference from GPS measurement outliers. For underwater missions, without the assistance of additional underwater absolute positioning measurements, the unknown errors introduced by the navigation system modeling process and state estimation process will affect the navigation accuracy. Therefore, in this paper, we propose an integrated navigation method for small-sized AUV in shallow-sea applications. Firstly, a robust GPS/Attitude and Heading Reference System (AHRS)/Doppler Velocity Log (DVL) navigation method for surface missions is proposed to maintain high-frequency output and robustness. Secondly, a Multiple Model (MM) integrated navigation method is proposed for AUV underwater missions. An AHRS/DVL/NavNet sub-model is proposed to perform measurement correction to correct the unknown errors. At the same time, an adaptive AHRS/DVL sub-model based on Variational Bayesian (VB) is presented to estimate the time-varying measurement noise covariance to improve the state estimation accuracy without the extra external observations. Two sub-models run parallel based on the Interacting Multiple Model (IMM). The effectiveness of the proposed integrated navigation method is verified by actual sea trial data from Sailfish 210 AUV.

Optimizing wireless acoustic energy harvesting and communication with passive markers underwater

To enhance functionality of Acoustic Identification Tags (AID), passive markers designed as landmarks for Autonomous Underwater Vehicles (AUVs) [Satish et al., JASA 149 (2021)], the development of an accompanying battery free active communication platform was explored [Bhardwaj et al., JASA 152, 4 (2022)]. This platform utilizes custom piezoelectric transducers impedance matched for broadband operation, enabling concurrent acoustic energy harvesting and fast backscatter communication [Allam et al., SPIE 31, 9 (2022)]. The combination of these active and passive devices can improve AUV localization and information transfer for specific applications such as short-range search missions or AUV homing and docking. The design of this platform for underwater operations requires a balance of parameters such as center frequency and piezoelectric element dimensions to achieve a high range of operation, power efficiency and data rates, while minimizing attenuation and spreading losses challenges. The design process for piezo selection, acoustic and electrical impedance matching, transducer casing design, and the experimental evaluation of the chosen 350 kHz design will be discussed. This approach was validated experimentally to quantify performance metrics such as the maximal range, data rates using various standard communication such as Frequency Shift Keying (FSK) and Amplitude Modulation (AM).

Improving passive acoustic target detection using machine learning classifiers

The ocean covers 70% of the Earth surface area, yet over 80% of it remains unexplored despite the advances in underwater acoustics and oceanography. Ocean exploration is critical for accurate climate model development, renewable energy applications, and in understanding the marine habitat. Further exploration necessitates improvements in underwater navigation with Autonomous Underwater Vehicles (AUVs). Utilizing acoustic landmarks can enhance AUV localization performance [Fula et al., Oceans IEEE/MTS (2018)]. Passive markers called Acoustic Identification (AID) Tags have unique and identifiable acoustic reflection signatures designed to function as landmarks [Satish et al., JASA 149 (2021)]. These targets can be detected by Match Filtering returns with template signals. Match Filtering performance is limited in the presence of strong interfering signals, and with changes in sound speeds that alters the temporal structure of these signatures. The application of a Machine Learning classifiers for detecting AID tag signatures can improve the localization performance. Through implementation of Logistic Regression, Deep Neural Networks, and Convolutional Neural Networks, the generalizability and superiority of these models is demonstrated. We report high accuracies (>90 %) in a multilabel (8 classes) classification task with signals with low SNR (–6 dB) and strong interference (+12 dB).

Evaluation of UKF-Based Fusion Strategies for Autonomous Underwater Vehicles Multisensor Navigation

In the underwater domain, guaranteeing accurate navigation for an autonomous underwater vehicle (AUV) is a complex but fundamental task to be achieved. As a matter of fact, only by ensuring a correct AUV localization, it is possible to accomplish surveillance, monitoring, and inspection missions. Most of the navigation filters for AUVs are based on Bayesian estimators, such as the Kalman filter, the extended Kalman filter (EKF), or the unscented Kalman filter (UKF), and employ different instruments, including the Doppler velocity log to perform the localization task. Recently, the use of payload sensors, such as cameras or forward-looking SONARs, in navigation-aiding has arisen as an interesting research field in the attempt to reduce the localization error drift. Such sensors, if used simultaneously, can provide multiple observations, which can be combined in a Kalman filtering framework to increase navigation robustness against noise sources. Navigation techniques that employ multiple devices can provide a high improvement of the estimation quality, but they can also cause an increase in terms of computational load. Consequently, strategies that can represent a tradeoff between these two conflicting goals have to be investigated. In this contribution, two different frameworks have been implemented and compared: on the one hand, a centralized iterative UKF-based navigation approach and, on the other hand, a sensor fusion framework with parallel local UKFs. The sequential (or iterated) UKF, where the correction step is iteratively performed for each available measurement, belongs to the first class of filters. The federated and the consensus-based decentralized UKFs can be categorized as the second class and they differ in the employed fusion strategy. Experimental navigation data obtained during sea trials performed at Vulcano Island, Messina, Italy has been used for offline validation. The results analysis focuses on both the navigation quality and the filter robustness against the reduction of the available measurements.

A Variational Bayesian-Based Simultaneous Localization and Mapping Method for Autonomous Underwater Vehicle Navigation

Simultaneous Localization and Mapping (SLAM) is a well-known solution for mapping and realizing autonomous navigation of an Autonomous Underwater Vehicle (AUV) in unknown underwater environments. However, the inaccurate time-varying observation noise will cause filtering divergence and reduce the accuracy of localization and feature estimation. In this paper, VB-AUFastSLAM based on the unscented-FastSLAM (UFastSLAM) and the Variational Bayesian (VB) is proposed. The UFastSLAM combines unscented particle filter (UPF) and unscented Kalman filter (UKF) to estimate the AUV poses and features. In addition, to resist the unknown time-varying observation noise, the method of Variational Bayesian learning is introduced into the SLAM framework. Firstly, the VB method is used to estimate the joint posterior probability of the AUV path and observation noise. The Inverse-Gamma distribution is used to model the observation noise and real-time noise parameters estimation is performed to improve the AUV localization accuracy. Secondly, VB is reused to estimate the noise parameters in the feature update stage to enhance the performance of the feature estimation. The proposed algorithms are first validated in an open-source simulation environment. Then, an AUV SLAM system based on the Inertial Navigation System (INS), Doppler Velocity Log (DVL), and single-beam Sonar are also built to verify the effectiveness of the proposed algorithms in the marine environment. The accuracy of the proposed methods can reach 0.742% and 0.776% of the range, respectively, which is much better than 1.825% and 1.397% of the traditional methods.

An Adaptive Filtering Method for Cooperative Localization in Leader-Follower AUVs.

In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader–follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage–Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative localization of multi-AUVs. Firstly, the measurement anomalies were evaluated by calculating the Chi-square test statistics based on the innovation. The detection threshold was determined according to the confidence level of the Chi-square test, and the Chi-square test statistics exceeding the threshold were regarded as measurement abnormalities. When measurement anomalies occurred, the Sage–Husa adaptive extended Kalman filter algorithm was improved by suboptimal maximum a posterior estimation using weighted exponential fading memory, and the measurement noise covariance matrix was adjusted online. The numerical simulation of leader–follower multi-AUV cooperative localization verified the effectiveness of the improved SHAEKF and demonstrated that the average root mean square and the average standard deviation of the localization errors based on the improved SHAEKF were significantly reduced in the case of the presence of measurement abnormalities.

Influence of Integration Schemes and Maneuvers on the Initial Alignment and Calibration of AUVs: Observability and Degree of Observability Analyses.

The use of autonomous underwater vehicles (AUV) has increased in a wide range of sectors, including the oil and gas industry, military, and marine research. The AUV capabilities to operate without a direct human operator and untethered to a support vessel are features that have aroused interest in the marine environment. The localization of AUV is significantly affected by the initial alignment and the calibration of the navigation sensors. In this sense, this paper proposes a thorough observability analysis applied to the latter problem. The observability analysis is carried out considering three types of sensor fusion integration and a set of maneuvers, and the results are validated through numerical simulations. As main contribution of this paper, it is shown how the addition of position errors in the observation vector can decouple some gyro and accelerometer biases from the latitude and altitude errors, particularly in the stationary observability analysis. The influence of oscillations in the diving plane and typical AUV maneuvers are analyzed, showing their relative impacts on the degree of observability of the inertial measurement unit (IMU)/Doppler velocity log (DVL) misalignment and DVL scale factor error. Finally, the state’s estimation accuracy is also analyzed, showing the limitation of the degree of observability as an assessment tool for the estimability of the states.

Cooperative Localization Based Multi-AUV Trajectory Planning for Target Approaching in Anchor-Free Environments

Target approaching is an essential ability in marine applications, such as underwater rescue. The recent advances in autonomous underwater vehicles (AUVs) make them ideal options to deliver the target approaching in anchor-free environments, where no device with a known position (anchor) is pre-deployed. To approach a moving target, the trajectory planning of AUVs is an indispensable technical guarantee. However, due to the lack of anchors and the error accumulation during the navigation, the position errors of AUVs grow without bound and degrade trajectory planning performance. Thus, an AUV localization procedure is required and should be used in combination with the trajectory planning. In this paper, we research the multiple-AUV trajectory planning based on AUV cooperative localization and propose a framework named multiple-AUV cooperative localization based trajectory planning (MCLTP). Under MCLTP, scalable cooperative localization for multiple AUVs (SCLMA) and trajectory planning for multiple AUVs (TPMA) algorithms are derived. The design of SCLMA focuses on improving the localization accuracy by alleviating the accuracy degradation caused by the accumulated errors in the inertial measurements. TPMA formulates the trajectory planning into a convex optimization problem with balanced target detection probability and tracking accuracy. Furthermore, the considerations of the practical constraints, such as the kinematic and communication limitations of AUVs, collision and obstacle avoidance, make TPMA reliable in the complex marine environments. Simulation results reveal the advantages of the proposed methods by comparisons with the state-of-the-art alternative algorithms.

A Novel ANFIS-AQPSO-GA-Based Online Correction Measurement Method for Cooperative Localization

To solve the effect of unreliable acoustic range measurements in cooperative localization of autonomous underwater vehicles, a novel localization method is proposed by using a hybrid metaheuristic algorithm that consists of the adaptive quantum-behaved particle swarm optimization (AQPSO) algorithm combined with the genetic algorithm (GA) to train the adaptive neurofuzzy inference system (ANFIS) model. The method can predict the acoustic ranging errors online, then correct the measurements, and improve the accuracy and stability of the cooperative localization system. We first tested the optimization ability of AQPSO-GA with an open-source dataset, and then, a large number of simulation experiments are performed with actual data collected in lake-water trials to fully compare and analyze the performance of various cooperative localization methods. The experimental results show that, compared with the cooperative localization method based on the cubature Kalman filter, the proposed method can reduce the localization error by 90% and effectively guarantee the stability of the cooperative localization system.

A Novel Simulation Platform for Underwater Data Muling Communications Using Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) are seen as a safe and cost-effective platforms for performing a myriad of underwater missions. These vehicles are equipped with multiple sensors which, combined with their long endurance, can produce large amounts of data, especially when used for video capturing. These data need to be transferred to the surface to be processed and analyzed. When considering deep sea operations, where surfacing before the end of the mission may be unpractical, the communication is limited to low bitrate acoustic communications, which make unfeasible the timely transmission of large amounts of data unfeasible. The usage of AUVs as data mules is an alternative communications solution. Data mules can be used to establish a broadband data link by combining short-range, high bitrate communications (e.g., RF and wireless optical) with a Delay Tolerant Network approach. This paper presents an enhanced version of UDMSim, a novel simulation platform for data muling communications. UDMSim is built upon a new realistic AUV Motion and Localization (AML) simulator and Network Simulator 3 (ns-3). It can simulate the position of the data mules, including localization errors, realistic position control adjustments, the received signal, the realistic throughput adjustments, and connection losses due to the fast SNR change observed underwater. The enhanced version includes a more realistic AML simulator and the antenna radiation patterns to help evaluating the design and relative placement of underwater antennas. The results obtained using UDMSim show a good match with the experimental results achieved using an underwater testbed. UDMSim is made available to the community to support easy and faster evaluation of underwater data muling oriented communications solutions and to enable offline replication of real world experiments.

Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples

Underwater acoustic communication is a key point for performance improvement in an autonomous underwater vehicle (AUV) swarm. The communication process is essential for improving the AUVs localization accuracy for navigation and is a convenient way for sharing information among the AUVs in a network. The objective of this work, which was developed in the COMET and NEMOSENS projects, is to address the communication process required in a mobile underwater wireless network, with a focus on the proposal of an adaptive physical layer methodology. We discuss about the employed channel access method, the frame structure, and we propose the usage of an adaptive guard interval in order to ameliorate the network usage rate. We explain the physical layer aspect of the communication: the data processing at the transmitter and receiver side. In addition to that, we propose the usage of smart communications among AUVs. We design a method for adapting some physical layer parameters. The proposed approach relies only on the knowledge of the transmission geometry, and it optimizes the number of subcarriers and the cyclic-prefix length of the Orthogonal Frequency-Division Multiplexing (OFDM) system. The obtained results show a performance improvement in terms of bit-error rate when compared with the case of random parameters selection. These results corroborate the benefits of our adaptive parameters approach.

An Improved Variational Adaptive Kalman Filter for Cooperative Localization

In this paper, an improved variational adaptive Kalman filter for cooperative localization with inaccurate prior information is proposed, in which the prior scale matrix of the one-step prediction error covariance matrix is adaptively estimated by using the expectation-maximization algorithm. A novel alternate iteration strategy is proposed to reduce the computational complexity of the proposed method. Convergence analysis and theoretical comparisons with the existing advanced adaptive Kalman filtering methods are also provided. Based on this, a new master-slave cooperative localization method is proposed. Lake experiment results of cooperative localization for autonomous underwater vehicles demonstrate the advantages of the proposed method over existing methods. Compared with the cutting-edge adaptive master-slave cooperative localization method, the proposed method has been improved by 22.52% in average localization error but no more than twice computational time is needed.

A Novel Measurement Information Anomaly Detection Method for Cooperative Localization

To deal with the influence of abnormal underwater acoustic ranging errors in cooperative localization of autonomous underwater vehicles, a novel measurement information anomaly detection method based on adaptive neuro-fuzzy inference system (ANFIS) is proposed. The method can accurately identify and isolate acoustic distance information with errors exceeding the threshold range even when multiple distance information is alternately used as the measurement data for the filtering algorithm. The adaptive cubature Kalman filter is used to extract the characteristic information, which can better reflect the change of measurement information. According to the preset state threshold, the flag bit obeying Bernoulli distribution is obtained, and the hybrid database is established. This method combines the online data training mechanism with the ANFIS-based detection system to update the ANFIS rules online, which can effectively improve the reliability and accuracy of anomaly detection method, especially when the sample data are insufficient. Experimental results based on data obtained from the actual lake-water trial show that the method can accurately identify the abnormal acoustic distance information and retain the accurate distance information to ensure the stable operation of cooperative localization system.

A Novel Adaptive Filter for Cooperative Localization Under Time-Varying Delay and Non-Gaussian Noise

In this study, to cope with time-varying delay in acoustic communication under non-Gaussian noise caused by outliers, a novel robust delay filtering algorithm is proposed for cooperative localization of autonomous underwater vehicles (AUVs). First, the modified measurement equations of the nonlinear cooperative location system with time-varying delay are derived, and the delay caused by the information processing and propagation of the underwater acoustic modem is converted into measurement bias. Second, to improve the robustness of the system with outliers caused by abnormal measurement values of underwater acoustic communication and Doppler velocity log (DVL), the statistical similarity measure (SSM) is introduced to construct the cost function. This is accomplished by quantifying the similarity between the state vector and the predicted state vector, similarity between the delay measurement, and modified predicted delay measurement. Finally, the modified measurement noise variance is obtained by maximizing the cost function, and two algorithms for the approximate solution of the nonlinear cost function are presented. The proposed robust delayed algorithm alleviates the impacts of delayed measurement with outliers on localization accuracy. The effectiveness and potential of the proposed filter are verified by lake trials.

Maximum A Posteriori estimation for AUV localization with USBL measurements

Due to the limitations of electromagnetic signals, underwater scenarios increase the complexity of developing accurate navigation systems. In the last decades, Ultra-Short BaseLine (USBL) positioning systems have been widely and efficiently used for Autonomous Underwater Vehicles (AUVs) localization, endorsing to be a suitable solution to limit the navigation drift without requiring periodic surfacing for Global Positioning System (GPS) resets. Typically, in the localization context, USBL measurements are exploited as observations within the on-board navigation filter where, most of the time, Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) solutions are employed. In a break-away from the above-mentioned approaches, in this study, the localization task is solved as a Maximum A Posteriori (MAP) estimation problem. The presented solution is validated through the use of data gathered in October 2020 during EUMarineRobots (EUMR) tests in La Spezia (Italy) within the activities of the SEALab, the joint research laboratory between the Naval Support and Experimentation Center (Centro di Supporto e Sperimentazione Navale, CSSN) of the Italian Navy and the Interuniversity Center of Integrated Systems for the Marine Environment (ISME).

Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships

This paper introduces the localization method of an Autonomous Underwater Vehicle (AUV) in environments (such as harbors or ports) where there can be passing ships near the AUV. It is assumed that the AUV can access the trajectory and approximate source level of a passing ship, while identifying the ship by processing the ship’s sound. This paper considers an AUV which can localize itself by integrating propeller and Inertial Measurement Units (IMU). Suppose that the AUV has been moving in underwater environments for a long time, under the IMU-only localization. To fix long-term drift in the IMU-only localization, we propose that the AUV localization uses sound measurements of passing ships whose trajectories are known a priori. As far as we know, this AUV localization method is novel in using sound measurements of passing ships of which the trajectories are known a priori. The performance of the proposed localization method is verified utilizing MATLAB simulations. The simulation results show significant estimation improvements, compared to IMU-only localization. Moreover, using measurements from multiple ships gives better estimation results, compared to the case where the measurement of a single ship is used.

A Novel Heavy-Tailed Mixture Distribution Based Robust Kalman Filter for Cooperative Localization

In cooperative localization for autonomous underwater vehicles (AUVs), the practical stochastic noise may be heavy-tailed, and nonstationary distributed because of acoustic speed variation, multipath effect of acoustic channel, and changeable underwater environment. To address such noise, a novel heavy-tailed mixture (HTM) distribution is first proposed in this article, and then expressed as a hierarchical Gaussian form by employing a categorical distributed auxiliary vector. Based on that, a novel HTM distribution based robust Kalman filter is proposed, where the one-step prediction, and measurement likelihood probability density functions are, respectively, modeled as an HTM distribution, and a Normal-Gamma-inverse Wishart distribution. The proposed filter is verified by a lake experiment about cooperative localization for AUVs. Compared with the cutting-edge filter, the proposed filter has been improved by 50.27% in localization error but no more than twice computational time is required.