Velocity Log Research Articles

Tightly Coupled INS/APS Passive Single Beacon Navigation

Unlike aerial or terrestrial navigation, the global navigation satellite system (GNSS) is not available underwater. This is a big challenge for underwater navigation. The inertial navigation system (INS) aided by the single-beacon acoustic positioning system (APS) provides one solution, but the long-range case is limited by low-SNR conditions. Inspired by passive synthetic aperture detection, we proposed a new tightly coupled navigation algorithm based on spatial synthesis and one-way-travel-time (OWTT) range measurement. We design two estimators: the DOA/range estimator using the model-based method and the tightly coupled INS/APS navigation estimator. Based on the improved UKF, all information is combined. Simulation is carried out in MATLAB. Compared with range-only tightly coupled INS/APS navigation, synthetic long baseline (SLBL) algorithm and Doppler velocity logger (DVL) aided centralized extended Kalman filter (CEKF) based single beacon INS/OWTT navigation, the proposed method’s performance is proven. The main contributions of this work are: (1). Propose a new architecture of underwater integrated navigation; (2). Apply the passive acoustic detecting method in the navigation to improve accuracy. (3). Apply the tightly coupled method to improve availability.

Morphology identification of gas hydrate based on a machine learning method and its applications on saturation estimation

SUMMARY Proper identification of hydrate morphology is an essential pre-condition for the quantification and exploitation of gas hydrate resources. However, the morphology results from core-based analysis and resistivity-based imaging could be discontinuous in hydrate-bearing intervals. Rock physical model-based methods could predict morphology within complete hydrate-bearing intervals, but the accuracy is not much satisfactory in some cases. In this study, we propose a machine learning (ML) method using the wavelet twin support vector machine (WTWSVM) to accurately differentiate the pore-filling and grain-displacing hydrate. By employing different combinations of well logs as the inputs of the WTWSVM, we find the optimal one for the data set in Hydrate Ridge, offshore Oregon is the combination of gamma-ray, resistivity, compressional and shear wave velocity logs, with an accuracy of 88.6 per cent and F1-score of 82.89 per cent. Compared with the two traditional rock-physics-based methods and three typical ML algorithms, the WTWSVM with those optimal inputs performs better in terms of accuracy and F1-score. We then use the WTWSVM to predict the hydrate morphology in the hydrate-bearing intervals at an unlabelled (i.e. unidentified hydrate morphology) site 1250F and a partially labelled (i.e. only a portion of the hydrate and its morphology is identified by IR images) site 1247B at Hydrate Ridge. Finally, the hydrate-morphology-related rock physics models are employed to construct 3-D crossplots of density, compressional and shear wave velocity, on which hydrate concentration, as well as other reservoir parameters, are estimated through projecting. The proposed WTWSVM method and workflow are proved to be valid based on the good agreement between the reservoir parameters from core measurement and elastic properties.

A Polar Integrated Alignment Assisted by DVL Under Large Azimuth Misalignment

The gravity tends to coincide with the earth’s rotation vector in the polar region, degrading the coarse alignment azimuth of the strapdown inertial navigation system (SINS) to a large misalignment. Under a large azimuth misalignment, the linear error model inevitably renders unacceptable theoretical errors in the integrated alignment. In addition, polar region challenge such sensors as a star sensor of celestial navigation, which fails to provide high-precision attitude reference for integrated alignment. Therefore, in allusion to the misalignment mentioned above based on SINS for the polar region, an integrated alignment algorithm is proposed, assisted by the Doppler Velocity Log (DVL). Notably, the work first investigates the extent to which azimuth accuracy deteriorates in the polar region. On the premise of large azimuth misalignment, the nonlinear error model under the transverse frame is derived from the ellipsoid earth model. Additionally, the state and measurement models are constructed, combined with the velocity of DVL as an observation. An adaptive measurement variance matrix is designed for the unscented Kalman filter (UKF) to estimate the attitude error. Eventually, the semi-physical experiments demonstrate that the proposed algorithm significantly improves the initial alignment accuracy of the polar region.

An experimental comparison of Deep Learning strategies for AUV navigation in DVL-denied environments

Accurate and robust navigation and localisation systems are critical for Autonomous Underwater Vehicles (AUVs) in order to perform missions in challenging environments. However, since the Global Positioning System (GPS) is not available in the underwater domain, the localisation task is commonly fulfilled by integrating direct linear speed readings provided by a Doppler Velocity Log (DVL) over time. As a consequence, DVL failures or fallacies and DVL-denied environments may arise as unexpected causes for severe malfunctions of the whole navigation system. Motivated by these considerations and the outstanding performance of Deep Neural Networks (DNNs) in supervised regression problems, a Deep Learning (DL) -based approach has been developed to estimate the vehicle’s body-frame velocity, without canonically employing DVL measurements, in a Dead-Reckoning (DR) navigation strategy. In particular, this work will describe the whole framework, starting from the data gathered by the AUVs of the National Oceanography Centre (NOC) during different field campaigns, through to the data pre-processing and the inference of the predicted velocity. Finally, a comprehensive offline comparison between different DL-based models is presented to assess the validity of the proposed approach.

Velocity estimation using a compact correlation velocity log

Unmanned underwater vehicles require bottom-referenced acoustic navigation aids to maintain long-term positional accuracy without surfacing. When these platforms are small, they create new design constraints for acoustic navigation aids because of the limited available space and power. Traditional acoustic navigation techniques, such as Doppler Velocity Logs, are unsuitable for use on small platforms because of the power required to maintain adequate signal to noise ratio when they are scaled in size. A compact correlation velocity log (CVL) is an alternative approach that can meet the power, space, and accuracy requirements for an acoustic navigation aid on such platforms. This device uses a single projector, a sparse receive array, and estimates platform motion using a multi-dimensional fitting algorithm over an ensemble of 3 or more pings. This presentation will discuss the theory of operation, simulation, and experimental results for a 300 kHz compact CVL that is 4 × 8 cm2. [The authors want to acknowledge Lockheed Martin Rotary and Mission Systems for their financial support of this work.]

Acoustic receiving on glider-type autonomous underwater vehicles: Advantages and challenges

An ocean glider is a specific type of Autonomous Underwater Vehicle (AUV) that operates using a buoyancy engine rather than traditional propellors or thrusters. This method of propulsion enables a glider to be deployed for weeks or months and is relatively quiet, making the ocean glider a desirable platform for persistent acoustic monitoring. A glider typically remains submerged for several hours, during which time it can be challenging to localize the vehicle precisely. Gliders are low-power platforms that operate in the mid-water column, and therefore subsea navigation technologies implemented on other types of AUVs, such as inertial navigation systems aided by Doppler velocity logs, may not be suitable. Acoustic signals from fixed broadband sources have been used for subsea localization of Seaglider, a commercially available glider platform. Measurements of the multipath acoustic arrival structure received on Seagliders at ranges up to hundreds of kilometers from transmitting sources were used for vehicle localization in both temperate and polar underwater sound propagation environments. Methodology and results for subsea localization of the Seaglider platform will be presented and placed in the context of a broader discussion of the advantages and challenges specific to the glider as a moving acoustic receiving platform.

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.

Robust estimation of shear wave velocity in a carbonate oil reservoir from conventional well logging data using machine learning algorithms

In recent decades, various applications of shear wave velocity have been reported in oil and gas projects. These applications include the determination of lithology, estimation of geomechanical parameters, reservoir fluid detection, etc. The shear wave velocity is usually measured or recorded by DSI (Dipole Shear Sonic imager) instruments. This log is not usually available in all wells of a hydrocarbon field because of the high cost of running this log compared to other conventional logs. However, it is tried to estimate the shear wave velocity from other related logs.The literature contains many empirical rock physics models used to estimate shear wave velocity. Most of them were developed for sandstone lithologies and didn't applicable in carbonate reservoir because of existing some vertical and horizontally complex fracture networks in the carbonate reservoirs that cause a quite different behavior for the shear wave velocity.The main objective of this research is to concentrate on the effect of lithology in the estimation of the target log for a carbonate reservoir. At the earliest step, preprocessing is performed and 455 data points of several conventional well logs, i.g., acoustic, density, neutron porosity, resistivity, gamma-ray, calcite volume, dolomite volume, and water saturation logs have been applied to generate the synthetic shear wave velocity log in a carbonate oil reservoir.First, original data are divided into training, validation, and testing subsets. Different data-driven predictive models were built: Multiple Linear Regression (MLR), Ensemble Learning Method (ELM), and three architectures of Artificial Neural Networks (ANN). The performance of all techniques was compared with each other. Finally, the feed-forward neural network shows the highest accuracy with R-values of 0.99 and 0.96 for the training and testing datasets. Moreover, the network design and tuning parameters of the model are adjusted using the grid search optimization technique to obtain the optimal design of the artificial neural network for estimating the shear wave velocity log. Hence, a deep artificial neural network, i.e., [8–11–15-1] network with bayesian regularization training algorithm and hyperbolic tangent sigmoid transfer function, is proposed to predict the target response in other wells.

Illumination compensation for the adjoint-state first-arrival traveltime tomography in VTI media

The sensitivity kernel of each parameter is angle-dependent in an anisotropic traveltime multiparameter inversion. To analyze the angle illumination in a transversely isotropic media with a vertical symmetry axis (VTI) by the adjoint state traveltime tomography (AST) method, a circular observation system is designed with receivers radially distributed at different propagated angles. Then, the nonuniform angular illumination in the gradient of different parameters in the VTI AST is revealed. The nonuniform angular illumination seriously hinders the simultaneous updates of multiple parameters toward the true solution and decreases the accuracy of the anisotropic multiparameter inversion. To overcome the illumination problem, a preconditioned AST method (PAST) is proposed by utilizing the diagonal elements of the approximate Hessian matrix as the angle illumination compensation for multiparameter gradients in the VTI medium. The PAST method not only compensates for angular illumination but also balances the ray density and eliminates gradient singular values, which improves the inversion accuracy for multiple parameters. The BP synthetic model test shows that the PAST method based on angular illumination compensation can invert the VTI multiparameter models more accurately. Therefore, the PAST inversion model can predict first-arrival traveltime information more accurately. Field data application in the East China Sea indicates that the inverted velocity of the proposed PAST fits well with the logging velocity, and the inverted anisotropic parameter agrees well with the Backus average values. Consequently, the image quality based on the PAST inverted models is slightly improved. It is proven that the proposed PAST method can better realize the near-surface multiparameter inversion of the VTI medium.

Evaluating uncertainty in empirically derived unconfined compressive strength (UCS) estimates and implications for drilling applications; a case study from the Cooper Basin

Accurate estimates of Unconfined Compressive Strength (UCS) are essential for a range of subsurface applications, including drilling wells for subsurface fluid extraction or injection. However, measuring UCS of subsurface rock samples through laboratory-based uniaxial and triaxial testing is time consuming, expensive, and potentially subject to individual sample variance. P-wave velocity logs are routinely obtained in petroleum basins and are commonly used to estimate UCS using empirically-derived correlations. In this study we analysed P-wave velocity data from 43 wells in the Cooper Basin, Australia and created mean ranges of expected UCS through the Tirrawarra, Toolachee, Patchawarra, Murteree Shale and Epsilon formations, using literature-derived P-wave velocity-UCS correlations, and also estimated UCS based on selected available laboratory-determined P-wave velocity measurements. These two suites of P-wave velocity-derived estimates of UCS are then compared to available results from laboratory uniaxial testing of core samples. Our analysis indicates that P-wave velocities, and subsequent derived estimates of UCS from P-wave wireline logs, are typically lower than those from laboratory-analysed samples. This may reflect a sampling bias towards the selection of strong rocks for laboratory UCS testing or lower estimates of UCS due to wellbore damage and pore space content (air or liquids) from P-wave log derived velocities. However, the laboratory-derived P-wave velocities generally agree with laboratory-derived UCS data from the Patchawarra, Murteree Shale and Epsilon formations when using published empirical velocity-strength correlations. A retrospective case study presents the impacts of different UCS estimates on mud weight required to produce observed borehole breakouts in the Epsilon Formation. Breakouts were observed 90 degrees to the orientation of SHmax (117° N) with a mean width of 51°, the breakouts were produced during drilling using a 9.7 ppg mud weight. The back-calculated estimate of UCS from the observed breakout widths at 9.7 ppg is 154 MPa. This sits between the UCS estimate of 171 MPa from uniaxial testing and 145 MPa from the mean of laboratory sample-derived P-wave velocities. Estimates of UCS from empirical correlations based solely on p-wave log-derived are far lower and vary between 87.3 and 114.4 MPa.

A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization

The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors.

Feature-based Bathymetric Matching of Autonomous Underwater Vehicle Transects using Robust Gaussian Processes

Autonomous underwater vehicles have become essential tools for the collection of high-resolution bathymetric and geophysical data. An inertial navigation system, aided by a Doppler velocity log and a surface-mounted ultrashort baseline acoustic positioning system, is generally capable of providing sufficient accuracy at conventional survey scales. However, the accuracy decreases with depth, and the resulting relative positioning across different transects may not be sufficient to resolve features of interest at fine scales. This work presents a method for accurate coregistration of the position of adjacent transects. The approach is based on detecting and matching local features in overlapping multibeam echosounder swaths. The navigational errors for the transects are taken to be described by latent Gaussian processes, observed through these matches. The hyperparameters of the Gaussian process are learned from the data themselves and do neither require tuning of filter parameters nor intimate knowledge of the autonomous system or its sensor configuration. The proposed method is robust to outliers by considering a non-Gaussian observation model. The approach is demonstrated on a data set collected at the Arctic Mid-Ocean Ridge (AMOR). The method can be used to construct high-resolution bathymetric models from repeated passes over the same area or to accurately coregister other sensors such as cameras, subbottom profilers, and magnetometers. The primary contribution of this work is the application of feature-based matching of bathymetry with a robust Gaussian process.

SINS/DVL Linear Initial Alignment based on Lie Group SE3 (3)

This paper investigates the strapdown inertial navigation system (SINS) initial alignment aided by Doppler velocity log (DVL). A linear Kalman filtering method is proposed using the theory of Lie group, which is immune to the initial misalignment. The dead-reckoning position based on the body velocity provided by DVL and attitude provided by SINS is used as measurement. The SINS calculated attitude, velocity, position and DVL dead-reckoning position are formulated together as the elements of Lie group SE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> (3). Since the dead-reckoning position is a type of left-invariant measurement, the corresponding linear error-state model is derived based on the left-invariant group error definition. Although the Lie group state model is not group affine, thesmall attitude error assumption does not introduce accuracy degeneracy in deriving the linear error-state model. This is the core reason why the derived linear error-state model is still applicable for cases with large initial misalignment. Experiments are conducted to verify the feasibility and effectiveness of the proposed methods. The test results show that the proposed method has higher accuracy and faster convergence speed as compared with the existing methods.

INS/WT-DVL Integrated Navigation Algorithm Based on Improved Expected-Mode Augmentation for Underwater Vehicles

For underwater vehicles, the measurement accuracy of Doppler velocity log (DVL) working in water-track (WT) mode is affected by the ocean current, thereby affecting the performance of the inertial navigation system (INS)/WT-DVL integrated navigation system. As for the uncertainty of the ocean-current velocity model, we propose a novel integrated navigation algorithm based on an improved expected-mode augmentation (IEMA) algorithm, which is used to mitigate the effects of complicated ocean currents. First, we set up the basic fixed model set. Then, use the model set augmentation strategy to generate an expected model set that is more compatible with the actual ocean-current velocity model. Finally, use the expected model set to modify the estimation results of the basic fixed model set to acquire more precise ocean-current velocity data. In the proposed IEMA algorithm, a new model set augmentation strategy is designed by improving the existing expected-mode augmentation (EMA) algorithm. First, not only use the predicted probability, but also need to further use the measurement information at the current time to generate the expected model set. Second, multiple ocean-current velocity models (only one model in the existing EMA algorithm) are selected to form an expected model set, as a way to increase the proportion of expected models in the entire model set. Compared with the existing EMA algorithm, the IEMA algorithm not only has a smaller steady-state error but also substantially reduces the peak error. Simulated and experimental results indicate that the proposed IEMA algorithm can accurately estimate and compensate the ocean-current velocity more efficiently, and further improve environmental adaptability and enhance navigation accuracy.

Internal Model Control for AUVs with Output Time Delays and Input Disturbances

Autonomous Underwater Vehicles (AUVs) are increasingly being used for offshore inspection tasks. This paper investigates how navigation using Simple Internal Model Control for Proportional-Integral-Derivative Control (SIMC-PID) operates in a realistic offshore environment with waves and ocean current acting as input disturbances while the available underwater sensors introduce time delays on the output signals. First, the time delays are determined by investigating available absolute positioning sensor systems. Then, a model of the AUV and the external disturbances is established. The model-based SIMC-PID controller is tuned and examined based on acceptable disturbance rejection while tolerating the dominant time delays. Two simulation case studies show that the heave controller, in both cases, struggles to stabilize in 0 to 8 meters depths, while the surge and sway controllers tolerate the Doppler Velocity Log case (DVL) acceptably. Short Baseline (SBL) shows unacceptable performance in 0 to 15 meters depths. It is concluded that the simplicity of the SIMC-PID controller is an advantage and, therefore, useful when time delays are relatively small, but more advanced techniques must be applied for larger delays such as those introduced by SBL systems.

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.

Active and Passive acoustic logging applied to the detection of preferential flow in a sedimentary aquifer

Two boreholes of an experimental site located in the Cher region (France) were investigated via Full Waveform Acoustic Logging (FWAL). The acoustic tool used for the FWAL experiments is a flexible monopole tool holding two pairs of piezoelectric receivers and a magnetostrictive transducer. The tool was modified to perform both active and passive FWAL. To our knowledge, this change is a novelty. For passive acoustic logging, several runs were recorded to obtain a set of acoustic noise sections from which noise Root Mean Squared (RMS) amplitude logs and spectral amplitude logs in different frequency bandwidths were computed. The acoustic logs resulting from passive acoustic monitoring were compared with P-wave acoustic velocity, core data, and a flowmeter log. It is shown that: (1) the distribution of noise frequencies in the 0–5 kHz is strongly correlated with the variations of the flowmeter, (2) the distribution of noise frequencies and noise RMS amplitude is correlated with the lithology (core description), and the P-wave velocity log. As the noise is simultaneously recorded by two receivers of the tool, an interference noise section was elaborated by correlating and summing pairs of acoustic traces at each depth. This procedure, which can be interpreted as an interferometry analysis, points out the presence of low-frequency waves identified as Stoneley waves. It is shown that the Stoneley wave velocity obtained in passive mode can be used to estimate the shear velocity of the formation.

Virtual Beam Aided SINS/DVL Tightly Coupled Integration Method With Partial DVL Measurements

Strapdown inertial navigation system (SINS)/ Doppler velocity log (DVL) tightly coupled (TC) fusion is a popular scheme as it offers increased robustness compared to other approaches. In TC, DVL beam velocities are separately integrated with SINS in the navigation filter. To enhance the performance of SINS/DVL TC integration system in situations of partial DVL beam measurements, virtual beam aided solutions are proposed, theoretically derived, and experimentally evaluated. To that end, we analyze the geometrical relation between the DVL beam configuration and a zero velocity vector assumption and derive a zero velocity update (ZUPT) aided virtual beam method to continuously operate the TC fusion. Further, to avoid the precondition limitation of ZUPT method, a least squares support vector machine (LS-SVM) aided virtual beam construction algorithm together with its SINS/DVL TC integration scheme is proposed. Our experiments show the advantages and disadvantages of the ZUPT aided solution under different scenarios, and indicate the effectiveness of the LS-SVM aided virtual beam solution during short partial beam measurement periods.

Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

The error propagation of traditional strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) is not autonomous because its error state model is trajectory-dependence. Recently, the invariant error defined on the Lie group has raised much attention due to its trajectory-independent and autonomous error propagation. In this article, the invariant error-based Kalman filter for the SINS/DVL integration solution is investigated with a main focus on its extension and comparison. The contributions of this study are threefold. First, the invariant error defined on the matrix Lie group (group of double direct isometrics) is extended to model the nongroup affine traditional SINS mechanism and the group affine transformed SINS mechanism; both of them are derived in Earth frame and augmented with the drift and bias of the inertial measurement units (IMUs). Then, the observation equations for different invariant error-based state models are derived for SINS/DVL application, a theoretical analysis is performed, and a comprehensive evaluation is conducted under different maneuvering conditions by lake field trial. Finally, the variational Bayesian approach is introduced into the invariant error-based Kalman filter to infer the inaccurate process noise covariance matrix (PNCM) and time-varying measurement noise covariance matrix (MNCM) from a practical perspective; experimental results demonstrate that it can improve the navigation accuracy significantly. This study is expected to facilitate the selection of appropriate invariant error to SINS/DVL applications.

Automated Tuning of Nonlinear Kalman Filters for Optimal Trajectory Tracking Performance of AUVs

The performance of navigation algorithms significantly determines the trajectory tracking accuracy of the guidance, navigation, and control (GNC) system of an autonomous underwater vehicle (AUV). In closed-loop operation, the interaction among path planning, control, and navigation plays a crucial role in the tracking accuracy of the overall GNC system. A Doppler velocity log (DVL) is often used for AUVs to measure velocity over the ground, positively affecting the closed-loop tracking error. However, a DVL may not be installed in miniaturized AUVs due to limited space and energy. In this paper, a navigation filter for an underactuated miniature AUV (nanoAUV) is considered that is mainly based on acoustic localization using a novel highly-miniaturized ultra-short baseline (USBL) system and a depth pressure sensor. The nanoAUV is being developed for subglacial lake exploration. We compare two unscented Kalman filters (UKF) with different prediction models - the classical strapdown inertial navigation systems (SINS) model and a hydrodynamic motion model (HMM). To enable a fair comparison, filter parameters are auto-tuned with Bayesian optimization (BO) for open-and closed-loop performance, which is novel in AUV navigation. The results indicate that BO performs similarly to particle swarm optimization (PSO) regarding sample efficiency for the proposed problem. To quantify the GNC tracking performance, we use extensive Monte Carlo simulations. Results suggest that with BO-tuned navigation filter parameters, the median tracking error is reduced by up to 50% compared to default parametrization.