Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

In recent years, precious observations by autonomous underwater vehicle (AUV) have been performed. Tracking and navigation of AUV are important for precious observations. In Japan agency for Marine-earth Science and Technology (JAMSTEC) the observations with AUV ldquoURASHIMArdquo have been implemented in deep ocean area. AUV URASHIMA is navigated by an inertial navigation system (INS) mainly. The absolute position of AUV is tracked on the support vessel with super short base-line (SSBL) method of acoustic positioning. And sometime the own position in AUV is updated by the SSBL estimation result to correct the drift error of INS. However the SSBL is not so accurate. So the position correction includes the SSBL error and the position correction is performed drastically. In this paper, a method for accurate tracking of AUV and rapid correction of the INS status is proposed. The tracking is mainly achieved by SSBL on the support vessel. Additionally the INS data is transmitted from AUV including the acoustic positioning signal. The INS output of relative position to past own position is very accurate within short time. The INS data is used for tracking. Furthermore the position result estimated on the vessel is transmitted to AUV with next interrogation signal. And the own position in AUV is rapidly updated with accurate estimation. Some digital data including INS data is modulated with phase shift keying (PSK) method and direct sequence spread spectrum (DSSS) technique for transmission with acoustic positioning signal. The proposed method was considered by simulation. And good results were obtained.

The objective of this thesis is to contribute to Autonomous Underwater Vehicle (AUV) navigation by improving vehicle localisation accuracy when Doppler Velocity Log (DVL) bottom-tracking is unavailable. The Inertial Navigation System (INS) based localisation solution is prone to extreme uncertainties due to double integration of inherent errors within the INS acceleration measurements, unless the solution being externally aided (e.g. velocity measurements using the DVL bottom-track). As a solution for this, an improved model-aided INS localisation technique is introduced, which is complimented with the development of a novel model calibration and new water column velocity estimation method. The techniques established in this project are tested and validated using experimental data from a set of field manoeuvres using a <i>Gavia</i> class AUV and the performance is compared against other commonly used localisation methods. A baseline mathematical model was developed in this work using system identification to predict the motion response of the AUV based on its control commands. However, such models are generally calibrated for low water column velocities and a standard vehicle configuration, and are limited in application for variations in environmental conditions. To address this limitation, a novel model calibration technique was established to field calibrate the parameters within the baseline model to the current operating condition and vehicle configuration. Model calibration improved the results of the baseline model up to 73% when operating in low energy environments and the AUV position can be computed within an uncertainty range of 1.5% of the distance travelled. In comparison, uncertainties of conventional non-bottom-tracking localisation techniques could be up to 10% in similar environmental conditions. A secondary approach is also presented to determine the hydrodynamic coefficients of the mathematical model using Computational Fluid Dynamics (CFD) simulations and captive model experiments when the AUV is operating in complex flow conditions. A non-acoustic method was introduced to estimate the velocity of the water column using the motion response of the AUV. This method is capable of accurately estimating water column velocities in proximity to the AUV (i.e., the water column velocities at the same depth as the vehicle is), which is not typically resolved with existing methods such as acoustic Doppler current profilers. When the mathematical model-aided localisation solution is complimented with water column velocity prediction method, the localisation error is limited to less than 6% of the distance travelled even in extremely high currents (i.e. &gt;2 m s-1 in tested conditions). In such environmental conditions, the uncertainties of other commonly used non-bottom-tracking localisation methods, as tested against in this work, such as DVL water-track mode and unaided INS could be above 30%. One of the key advantages of the proposed localisation technique is that it could be applied to any torpedo shaped AUV (for example, platforms such as <i>REMUS, Iver, Bluefin, Explorer</i>, etc.) of any configuration by simply conducting a set of established field manoeuvres to identify its mathematical model parameters. Further, additional sensors beyond a typical AUV navigational payload (i.e. global positioning, accelerometers and gyroscopes) are not required to implement this technique in an AUV. The localisation technique developed in this thesis is capable of improving the motion control and navigation solution of the AUV in the absence of DVL bottom-tracking, which is critical for the expansion of vehicle performance in extreme environmental conditions.

The ultra-low resolution underwater terrain maps of the Arctic region reduce the localization and navigation accuracy of the underwater vehicle relying on terrain-aided navigation. In this paper, we study the navigation ability of Autonomous Underwater Vehicles (AUVs) under the ultralow-resolution terrain map. Firstly, the low-resolution map is transformed into a continuous map by bilinear interpolation. Then, a Terrain-Aided Navigation (TAN) system based on the Particle Filter (PF) is constructed to estimate the state of AUV position by particles. Particles of a random distribution of fixed variance can effectively track targets. However, a fixed variance distribution is not well adapted to many different situations. To improve navigation accuracy and robustness, fuzzy logic is used to estimate the distribution variance of particles under the current terrain gradient dynamically. The simulation results show that our proposed Fuzzy-PF TAN system is robust under various current disturbance situations. The position error of our system is within a map resolution unit of 500 m.

This article presents a comparison of different path-planning algorithms for autonomous underwater vehicles using terrain-aided navigation. Four different path-planning methods are discussed: the genetic algorithm, the A* algorithm, the rapidly exploring random tree* algorithm, and the ant colony algorithm. The goal of this article is to compare the four methods to determine how to obtain better positioning accuracy when using terrain-aided navigation as a means of navigation. Each algorithm combines terrain complexity to comprehensively consider the motion characteristics of the autonomous underwater vehicles, giving reachable path between the start and end points. Terrain-aided navigation overcomes the challenges of underwater domain, such as visual distortion and radio frequency signal attenuation, which make landmark-based localization infeasible. The path-planning algorithms improve the terrain-aided navigation positioning accuracy by considering terrain complexity. To evaluate the four algorithms, we designed simulation experiments that use real-word seabed bathymetry data. The results of autonomous underwater vehicle navigation by terrain-aided navigation in these four cases are obtained and analyzed.

Deploying long‐range autonomous underwater vehicles (AUVs) mid‐water column in the deep ocean is one of the most challenging applications for these submersibles. Without external support and speed over the ground measurements, dead‐reckoning (DR) navigation inevitably experiences an error proportional to the mission range and the speed of the water currents. In response to this problem, a computationally feasible and low‐power terrain‐aided navigation (TAN) system is developed. A Rao‐Blackwellized Particle Filter robust to estimation divergence is designed to estimate the vehicle's position and the speed of water currents. To evaluate performance, field data from multiday AUV deployments in the Southern Ocean are used. These form a unique test case for assessing the TAN performance under extremely challenging conditions. Despite the use of a small number of low‐power sensors and a Doppler velocity log to enable TAN, the algorithm limits the localisation error to within a few hundreds of metres, as opposed to a DR error of 40 km, given a 50 m resolution bathymetric map. To evaluate further the effectiveness of the system under a varying map quality, grids of 100, 200, and 400 m resolution are generated by subsampling the original 50 m resolution map. Despite the high complexity of the navigation problem, the filter exhibits robust and relatively accurate behaviour. Given the current aim of the oceanographic community to develop maps of similar resolution, the results of this study suggest that TAN can enable AUV operations of the order of months using global bathymetric models.

To improve the navigation accuracy of AUV (autonomous underwater vehicle), SINS (strapdown inertial navigation system), GPS (global positioning system), DVL (Doppler velocity log) and TAN (terrain aided navigation) are adopted in the AUV integrated navigation system. The mathematic model of the AUV integrated navigation system and the observation model of the chosen navigation sensors are built according to the system simulation experiments data. An improved filter based on RBF neural network for adjusting the information sharing factors is designed and implemented in the AUV integrated navigation system. Simulation experiments are carried out according to the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy is improved substantially with the multiple sensors and federated filter in case that colored noise is engaged. The novel integrated navigation system is effective in prohibiting the divergence of the filter and improving fault tolerance ability and it greatly raises the precision of the navigation accuracy for the AUV integrated navigation system.

To solve the localization failure problem of terrain-aided navigation (TAN) system of the autonomous underwater vehicle (AUV) caused by large area of underwater flat terrain in the Arctic, a navigation system with relocation part is constructed to enhance the robustness of localization. The system uses particle filter to estimate the AUV’s position and reduce the nonlinear noise disturbance, and the prior motion information is added to avoid the mismatching caused by the similar altitude of low-resolution map. Based on the estimate data and the measured altitude data, the normalized innovation square (NIS) is used to evaluate the differentiation of terrain sequence, and the differentiation is used as a judgment of whether the AUV is in the switch location. A simulation experiment is carried out on the 500 m resolution underwater map of the Arctic. The results show that adding the prior motion information can restrain the divergence of the estimator; NIS can accurately reflect the sharp change of terrain sequence. After the relocation process, the AUV can still maintain the positioning accuracy within 2 km after running 50 km in the area including flat and rough terrain. This research solves the problem of localization errors in the Arctic flat terrain in the system level and provides a solution for the application of underwater navigation in the Arctic.

In recent years, terrain-aided navigation (TAN) has been rapidly developed for the localization of autonomous underwater vehicles (AUVs). TAN provides a position estimate for vehicles relative to a priori topographic map and is typically a nonlinear filtering model. Particle filtering has attracted considerable attention for addressing nonlinear and non-Gaussian challenges. However, it has problems with particle degradation and particle impoverishment. To tackle the problem in this study, the particle swarm optimization (PSO) optimization approach is employed to increase particle variety while driving particles towards the high probability zone through iterative updates. In addition, prior information is introduced to the particle fitness to reduce the interference of pseudo-peaks and increase the estimate precision. Finally, simulation tests are implemented to verify the effectiveness of the proposed method. Simulation results indicate that the improved GPSO-PF method performs better in the case of likelihood function with multiple peaks.

This paper addresses the region tracking control problem for an autonomous underwater vehicle (AUV) and proposes a prediction-based region tracking control (PRTC) scheme for AUV. In the PRTC scheme, the idea of prediction is adopted to solve the problems of overshoot and high energy consumption due to the lack of consideration of the large inertia of the AUV in the traditional scheme. The PRTC scheme predicts the future position of AUV through the past time-series position of AUV and the outer boundary of the desired region, and then designs the controller depending on the predicted results. Furthermore, the relationship between the desired region and the control output of the proposed PRTC scheme is studied. It is found that its control output amplitude is susceptible to the desired region range, resulting in output saturation. Therefore, this paper proposes a control law optimization scheme considering the desired region. This optimization scheme modifies the error signal in the control law of the PRTC scheme so that it is only related to the relative position of the desired region where the AUV is located. Finally, the proposed schemes are applied on the ODIN AUV, and the simulation results verify the feasibility of the proposed schemes.

Research on position correction method for AUV large depth navigation based on ranging positioning

The Monterey Bay Aquarium Institute (MBARI) has conducted high-resolution multibeam bathymetry, sidescan, and subbottom sonar surveys in the Gulf of California using an autonomous underwater vehicle (AUV) as part of a three-month, two-ship oceanographic expedition. The seafloor mapping AUV D. Allan B. was operated from R/V Zephyr, achieving 22 successful 17-hour missions during March and April 2012. The 1-m lateral resolution seafloor maps and subbottom profiles were used to guide subsequent ROV Doc Ricketts dives made from MBARI's R/V Western Flyer, which combined visual surveys, sampling, and in situ experimentation by several different science groups. The vehicle is a modularized Dorado Class 54 cm (21 inch) diameter, 5.18 meter (17 feet) length, 733 kg (1616 pounds) air weight. The interior of the AUV floods with water when launched; electronics are contained in either one-atmosphere hermetic housings or in pressure-compensated enclosures. The AUV is ballasted to be about 3.63 kg (8 pounds) positive in seawater. Flotation is accomplished with 6000 meter rated syntactic foam. The D. Allan B. has three mapping sonar systems onboard: a Reson 200 kHz multibeam echosounder, an Edgetech FS-AU Sonar Package which comprises a 100 kHz chirp sidescan, a 410 kHz chirp sidescan, and a 2-16 kHz chirp subbottom profiler. With the onboard Lithium Polymer battery packs, the Mapping AUV has an endurance of approximately 17 hours at a speed of 1.5 meters per second, providing a range of 55 to 85 kilometers. The mapping AUV is equipped with a Kearfott inertial navigation system (INS) with Doppler velocity log, a Paroscientific pressure sensor, and an ultrashort baseline (USBL) and acoustic modem for communications. It also has an Iridium Satellite Short Burst Data modem, a drop weight, radio direction finder, Homer Pro and visible strobe location aids. Propulsion is accomplished with a duct-protected articulated propeller which provides 52 Newtons (12lbf) of thrust at 300 rpm as well as elevator and rudder functions. Turning diameter is less than 20 meters; climb/dive rate is approximately 30 meters per minute. Launches and recoveries were made from the RV Zephyr, a 24m (79 foot) former pilot boat that operates with four crew and four science personnel. The Zephyr is outfitted with a Brook Ocean Technologies J-Frame crane for AUV launch and recovery. Once the AUV is in the water, the mission is sent from a computer on the Zephyr to the AUV over a wireless Freewave Ethernet link. At the start of the mission, the AUV spirals down to the start-of-line while being position-updated with GPS data from the Zephyr aided by the USBL system. A Bottom Pressure Recorder used to provide in situ tide data for bathymetry correction was deployed prior to all AUV missions, and recovered afterwards, using a Benthos Recoverable Transponder mounted to an MBARI-designed frame. The seafloor mapping data were processed using MB-System, an open source software package developed at MBARI and the Lamont-Doherty Earth Observatory. The first half of the mapping AUV surveys focused largely on four transform faults in the central and northern gulf. The combination of high-resolution bathymetry and subbottom profiles enabled the identification of active fault traces and zones of subsurface gas and hydrate. The second half of the Zephyr expedition focused on the Alarcon Rise, a volcanic spreading center. The new surveys provide the first 1-m scale bathymetry coverage of the neovolcanic zone along an entire ocean spreading center segment. The mapping data also identified the locations of several fields of hydrothermal vent chimneys, two of which were confirmed to be currently active by ROV dives.

Aiming at the precise positioning of autonomous underwater vehicle(AUV) during submerged operation for long periods of time,an underwater terrain matching positioning method based on maximal likelihood estimation(MLE) is proposed.In this method,multibeam echo sounder(MBES) is taken as the underwater terrain measuring means,and maximum likelihood algorithm is used for correlation analysis of terrain character.For the influence from false peaks of likelihood function at flat bottom area,the Fisher criterion is introduced to restrict it.The false peaks are eliminated effectively and the discrimination of flat bottom area is enhanced.The simulation results based on one electronic chart data and sea test data from a multibeam echo sounder show that the proposed method is effective and feasible.

Precise dynamic positioning of autonomous underwater vehicles (AUVs) faces significant challenges because of actuator nonlinearities such as input saturation and backlash, which degrade system performance and stability in subsea operations. This work proposes a disturbance observer-based event-triggered prescribed-time dynamic positioning (DOB-ETPTDP) control framework to address these issues. The scheme reformulates the AUV kinematics as a dynamic position error system, modeling actuator nonlinearities as a bounded lumped disturbance. A key innovation is the prescribed-time disturbance observer that ensures rapid and accurate disturbance estimation, enhancing robustness against unknown actuator dynamics. A prescribed-time backstepping-based control law uses these estimates, to guarantee the asymptotic convergence of the dynamic position error to zero, independent of initial conditions. An adaptive prescribed-time event-triggered mechanism further optimizes control efficiency by reducing update rates and preventing Zeno behavior. Numerical simulations verified the effectiveness of the proposed DOB-ETPTDP scheme. In terms of convergence speed, the proposed method achieved improvements of approximately 55% and 67% compared with the sliding mode and backstepping approaches, respectively. Regarding computational complexity, the proposed method reduced the average computational load by about 20%. Moreover, the average data transmission ratio was significantly reduced, conserving more than 65% of the communication resources relative to the backstepping strategy. Rigorous stability analysis validated the theoretical guarantees, and extensive simulations confirmed that the proposed DOB-ETPTDP approach ensures high-accuracy dynamic positioning with enhanced robustness under complex actuator constraints.

Inertial Navigation Systems (INS) have an unbounded type of error characteristics. In order to overcome this problem, aiding systems are used to fuse information obtained via additional sensors with the INS solutions. Terrain Aided Navigation (TAN) is an integrated solution method which exploits Digital Terrain Elevation Maps (DTEM) and on board altimeter data to aid INS in case of GNSS outage. In TAN applications, a position measurement is formed by comparing the terrain profiles, which are obtained by using on board altimeters, with a preloaded DTEM. In general, this position measurement is formed by finding the DTEM points which create minimum-mean-square error with the terrain profile that is measured with on board altimeters. Error on the formed position measurement varies with the terrain profile over which the airborne platform flies. This position measurement is used to keep INS errors bounded with various sensor fusion techniques, two of which are Extended Kalman Filtering (EKF) and Gaussian Sum Filtering (GSF). However, error on the position measurement may cause these techniques to produce very erroneous navigation data or to behave in a divergent manner. Probabilistic Measurement Method (PMM) offered in this paper results with more probable position measurements which have less errors. PMM method is used with an EKF structure and takes advantage of position error covariances that are readily available within the EKF. Joint Probability Density Function (JPDF) of horizontal error components are formed and used while the terrain profile is compared with DTEM in order to form a position measurement. In this paper, performances of EKF with conventional positioning, GSF, and EKF with PMM are investigated and evaluated for 10 different flight paths. Flight paths are chosen to have different terrain profiles and the performance evaluation is realized by comparing the mean horizontal and vertical position errors.

Towards Arctic AUV Navigation