Large Position Errors Research Articles

A Novel Algorithm for Enhancing Terrain-Aided Navigation in Autonomous Underwater Vehicles

The position error in an inertial navigation system (INS) for autonomous underwater vehicles (AUVs) increases over time. Terrain-aided navigation can assist in correcting these INS position errors. To enhance the matching accuracy under large initial position errors, an improved terrain matching algorithm comprising terrain contour matching (TERCOM), particle swarm optimization (PSO), and iterative closest contour point (ICCP), named TERCOM-PSO-ICCP, is proposed. Initially, an enhanced TERCOM with an increased rotation angle is utilized to minimize heading errors and reduce the initial position error. The similarity extremum approach evaluates the initial matching outcomes, leading to an enhanced accuracy in the initial results. Next, artificial bee colony (ABC)-optimized PSO is employed for secondary matching to further reduce the initial position error and narrow the matching area. Finally, the ICCP, using the Mahalanobis distance as the objective function, is applied for the third matching, leveraging the ICCP’s fine search capabilities. The effective combination of these three algorithms significantly improves the terrain-aided navigation matching effect. Two tests show that the improved TERCOM-PSO-ICCP effectively reduces the matching error and corrects the position of the INS.

Self-Calibration of INS/Odometer Integrated Navigation System With a Large Initial Position Error

Self-Calibration of INS/Odometer Integrated Navigation System With a Large Initial Position Error

A Cascade Gravity Matching Algorithm With Large Initial Position Error

A Cascade Gravity Matching Algorithm With Large Initial Position Error

Assessing Variations in the Predictive Skill of Ensemble Snowband Forecasts with Object-Oriented Verification and Self-Organizing Maps

Abstract We used object-oriented verification and self-organizing maps (SOMs) to identify patterns in environmental parameters correlating with mesoscale snowband predictive skill by the High-Resolution Ensemble Forecast (HREF) system between 2017 and 2022. First, HREF snowband forecasts for 305 banding events were verified based on similarities between forecast and observed feature properties. HREF members performed comparably, demonstrating large positional errors, but the non-time-lagged High-Resolution Rapid Refresh member demonstrated the greatest overall skill. Observed banding events were clustered by 500-hPa geopotential height anomalies, mean sea level pressure, vertical velocity, frontogenesis, and saturation equivalent potential vorticity from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 using SOMs. Clusters reaffirmed the presence of midlevel frontogenesis, ascent, and reduced stability in most banding cases, and the predominant synoptic environments conducive to band development. Clusters were compared to determine whether patterns in the variables were correlated with predictive skill. Strength of upward motion was correlated with skill, with the strongest upward motion cases verifying 10% better than the weakest upward motion cases due to smaller positional error. Additionally, events with a single region of strong upward motion verified better than events with disorganized, but comparably intense, upward motion. The magnitude of frontogenesis was uncorrelated with skill, but events with more upright frontogenesis collocated with the band centroid were better predicted than events with shallower slopes and low-level frontogenesis displaced toward warmer air. The skill variance associated with different vertical motion magnitudes could assist forecasters in modulating forecast confidence, while the most common types of errors documented here may be beneficial to model developers in refining HREF member snowfall forecasts. Significance Statement High-resolution numerical weather prediction (NWP) models generally have limited predictive skill for mesoscale snowband forecasts. Even so, some snowbands are forecast by NWP models with much greater skill than others. In this work, we apply artificial intelligence to group snowband events based on atmospheric conditions and then determine whether different groups are easier or harder for models to predict. Identification of these groups could help forecasters know when to trust or be skeptical of NWP output and help developers improve snowband formation processes in NWP models.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/ PDR/ geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning (PDR) and geomagnetic technology have the problems of large initial position error, low sensor accuracy, and geomagnetic mismatch. In this study, a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed, where Wi-Fi, PDR, and geomagnetic signals are integrated to improve indoor positioning performances. One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm. During the positioning process, an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively. The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected, which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm. In addition, this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving, which satisfies the real-time requirement of our fusion positioning approach. Through experimental verification, the average positioning accuracy of the proposed approach reaches 1.59 m, which improves 33.2% compared with the existing particle filter fusion positioning algorithms.

A SE(2)-based transfer alignment for large installation misalignment angle

Transfer alignment is the optimal solution for initial alignment of shipborne or airborne weapons. The need for transfer alignment is increasing at large installation misalignment angles. Traditional linear models have large attitude errors at large installation misalignment angles, nonlinear algorithms are computationally intensive and difficult to be widely used, and quaternion optimization algorithms are sensitive to noise. In this paper, the Lie group theory is introduced for the first time in the study of transfer alignment. The attitude and velocity are represented as the Group of Direct Planar Isometries (SE(2)), and then the SE(2)-based error vectors are constructed by log-linearization. The SE(2)-based linear transfer alignment algorithm is developed for large installation misalignment conditions. The computational effort of the proposed algorithm is very small due to its linear structure, and it has strong engineering application value. The simulation and field test are conducted to illustrate the effectiveness of the proposed transfer alignment algorithm.

Robust neuro-adaptive attitude control for high-precision Sun-pointing on a halo orbit

A novel neuro-adaptive control design approach is presented to maintain a high-precision Sun-pointing attitude of a spacecraft on a Sun–Earth L1 halo orbit. In the presence of parametric inaccuracies and unknown disturbances, the proposed method effectively learns the modeling uncertainty and adapts the control action to achieve its objective. First, large attitude errors are driven towards zero by applying a conventional back-stepping approach that uses the standard quadratic Lyapunov function (QLF). Once the errors are within the required error bounds, a barrier Lyapunov function-based (BLF) approach is excited, ensuring that the attitude errors remain constrained from thereon. Moreover, following the philosophy of meta-cognition, learning is purposefully stopped when the attitude error becomes too small but excited again whenever it grows large. The robustness of the proposed control law is illustrated by carrying out several simulations incorporating modeling uncertainties and disturbance torques, including high-magnitude solar flares. Finally, the effectiveness of the proposed BLF-based adaptive control law is demonstrated by comparing it with a QLF-based adaptive control law following the same philosophy.

Cooperative trajectory tracking control of MUSVs with periodic relative threshold event-triggered mechanism and safe distance

This paper addresses the problem of cooperative trajectory tracking control problem for multiple unmanned surface vehicles (MUSVs) with event-triggered mechanism and safe distance, proposes a novel event-triggered adaptive sliding mode control strategy via nonlinear tracking differentiators (NTD) and periodic relative threshold event-triggered mechanism (PRTETM). Firstly, the formation autonomous navigation system is constructed by multiple leader–follower cooperative structure to establish via the synchronization scheme that guarantee the safe distance each others. Next, a novel adaptive sliding mode control with the square root form of position error is introduced to prevent the oscillation or divergence of the system caused by the larger position error. Then, a concise adaptive control strategy to compensate for dynamic model uncertainties and external environmental disturbances. Meanwhile, the NTD is used to quickly track the differential signal and achieve filter performance. Besides, to obtain smaller update frequency and less energy consumption, the PRTETM without Zero behavior and input saturation are introduced to facilitate the triggering of control inputs and avoid the actuator physical constraints. Theoretical analysis proves that the proposed cooperative control system is uniformly ultimately bounded. Finally, numerical simulations and comparisons consolidate that the effectiveness of the above control scheme and superior in the fixed threshold scheme.

Assessment of camera focal length influence on canopy reconstruction quality

Unoccupied aerial vehicles (UAV) with RGB-cameras are affordable and versatile devices for the generation of a series of remote sensing products that can be used for forest inventory tasks, such as creating high-resolution orthomosaics and canopy height models. The latter may serve purposes including tree species identification, forest damage assessments, canopy height or timber stock assessments. Besides flight and image acquisition parameters such as image overlap, flight height, and weather conditions, the focal length, which determines the opening angle of the camera lens, is a parameter that influences the reconstruction quality. Despite its importance, the effect of focal length on the quality of 3D reconstructions of forests has received little attention in the literature. Shorter focal lengths result in more accurate distance estimates in the nadir direction since small angular errors lead to large positional errors in narrow opening angles. In this study, 3D reconstructions of four UAV-acquisitions with different focal lengths (21, 35, 50, and 85 mm) on a 1 ha mature mixed forest plot were compared to reference point clouds derived from high quality Terrestrial Laser Scans. Shorter focal lengths (21 and 35 mm) led to a higher agreement with the TLS scans and thus better reconstruction quality, while at 50 mm, quality losses were observed, and at 85 mm, the quality was considerably worse. F1-scores calculated from a voxel representation of the point clouds amounted to 0.254 with 35 mm and 0.201 with 85 mm. The precision with 21 mm focal length was 0.466 and 0.302 with 85 mm. We thus recommend a focal length no longer than 35 mm during UAV Structure from Motion (SfM) data acquisition for forest management practices.

An Improved ICCP-Based Underwater Terrain Matching Algorithm for Large Initial Position Error

The matching algorithm is a key technology in underwater terrain-relative navigation (TRN). An improved iterative Closest Contour Point (ICCP) matching algorithm is proposed to address the mismatch of the sequence mapping algorithm in the event of the significant initial position error. Based on the rich topographical information provided by the multi-beam echosounder (MBES) in a short time, the conventional ICCP method of searching for target contour points based on single-point matching is improved. In order to reduce the interference of heading error, the terrain distribution feature is modeled as a two-dimensional Gaussian distribution, and a terrain feature descriptor with rotation invariance is established. The similarity between the measured local topographic map and the reference digital topographic map (DTM) is evaluated by the terrain characteristics descriptor to derive the target path. The results of simulation and field tests demonstrate that the improved method can not only realize position matching under large initial errors but also improve the matching accuracy. Furthermore, the simulation and field tests show that the improved ICCP algorithm works well in both flat and rough areas.

Multi-Institutional Study of End-to-End Dose Delivery Quality Assurance Testing for Image-Guided Brachytherapy Using a Gel Dosimeter

To quantify dose delivery errors for high-dose-rate image-guided brachytherapy (HDR-IGBT) using an independent end-to-end dose delivery quality assurance test at multiple institutions. The novelty of our study is that this is the first multi-institutional end-to-end dose delivery study in the world. The postal audit used a polymer gel dosimeter in a cylindrical acrylic container for the afterloading system. Image acquisition using computed tomography, treatment planning, and irradiation were performed at each institution. Dose distribution comparison between the plan and gel measurement was performed. The percentage of pixels satisfying the absolute-dose gamma criterion was reviewed. Thirty-five institutions participated in this study. The dose uncertainty was 3.6% ± 2.3% (mean ± 1.96σ). The geometric uncertainty with a coverage factor of k = 2 was 3.5 mm. The tolerance level was set to the gamma passing rate of 95% with the agreement criterion of 5% (global)/3 mm, which was determined from the uncertainty estimation. The percentage of pixels satisfying the gamma criterion was 90.4% ± 32.2% (mean ± 1.96σ). Sixty-six percent (23/35) of the institutions passed the verification. Of the institutions that failed the verification, 75% (9/12) had incorrect inputs of the offset between the catheter tip and indexer length in treatment planning and 17% (2/12) had incorrect catheter reconstruction in treatment planning. The methodology should be useful for comprehensively checking the accuracy of HDR-IGBT dose delivery and credentialing clinical studies. The results of our study highlight the high risk of large source positional errors while delivering dose for HDR-IGBT in clinical practices.

Guidance for precision landing on asteroid using active hopping trajectory

In the landing mission on low-gravity asteroids, the probe may have a large position error when touching the surface or hop away from the desired target after a collision. To achieve a precision landing, a guidance method using an active hopping trajectory is proposed. In contrast to passive hopping, the active hopping strategy can reduce the landing errors by controlling the collision attitude sequence of the probe. The collision attitude sequence is determined by dynamically planning the hopping trajectory which consists of an active hopping phase and a braking phase. The active hopping phase achieves an expected range with a small number of hops, and the braking phase eliminates the probe's horizontal velocity and provides feedback for the range adjustment of the active hopping phase. The guidance method based on the hopping trajectory planning is then verified in a simulated asteroid landing scenario. The results show that the method is effective in reducing the landing errors, robust to the environmental parameter perturbations, and applicable to landings on inclined surfaces.

A novel time difference of arrival source localization method based on constrained scoring algorithm with a calibration emitter

In this paper, we focus on the radiation source localization problem based on time-difference-of-arrival (time difference of arrival (TDOA)) measurements in the presence of sensor position errors. In order to alleviate the estimation degradation in locating a radiation source caused by the sensor position uncertainty, we propose to use a single calibration source and develop a novel two-stage localization algorithm based on the constrained scoring algorithm (CSA). In the first stage, we use the information of the calibration source to reduce the sensor position deviation, and here the CSA method is utilised to solve a quadratic constrained maximum likelihood (ML) objective function which is formulated from a set of underdetermined pseudo-linear equations. In the second stage, using the updated sensor positions and the TDOA measurements of the radiation source, another ML estimation problem with quadratic constraints is formulated, and we again invoke the CSA method to jointly estimate the source and sensor positions. Moreover, the asymptotically optimal performance of the proposed algorithm is mathematically analysed and proved based on the first-order error analysis. The simulation results verify the excellent performance of our algorithm and also demonstrate its robustness of resisting large measurement noise and sensor position errors.

Comparison of the Prediction of Indian Monsoon Low Pressure Systems by Subseasonal-to-Seasonal Prediction Models

AbstractThis study analyzes the prediction of Indian monsoon low pressure systems (LPSs) on an extended time scale of 15 days by models of the Subseasonal-to-Seasonal (S2S) prediction project. Using a feature-tracking algorithm, LPSs are identified in 11 S2S models during a common reforecast period of June–September 1999–2010, and then compared with 290 and 281 LPSs tracked in ERA-Interim and MERRA-2 reanalysis datasets. The results show that all S2S models underestimate the frequency of LPSs. They are able to represent transits, genesis, and lysis of LPSs; however, large biases are observed in the Australian Bureau of Meteorology, China Meteorological Administration (CMA), and Hydrometeorological Centre of Russia (HMCR) models. The CMA model exhibits large LPS track position error and the intensity of LPSs is overestimated (underestimated) by most models when verified against ERA-Interim (MERRA-2). The European Centre for Medium-Range Weather Forecasts and Met Office models have the best ensemble spread–error relationship for the track position and intensity, whereas the HMCR model has the worst. Most S2S models are underdispersive—more so for the intensity than the position. We find the influence of errors in the LPS simulation on the pattern of total precipitation biases in all S2S models. In most models, precipitation biases increase with forecast lead time over most of the monsoon core zone. These results demonstrate the potential for S2S models at simulating LPSs, thereby giving the possibility of improved disaster preparedness and water resources planning.

Partition of Forecast Error into Positional and Structural Components

Weather manifests in spatiotemporally coherent structures. Weather forecasts hence are affected by both positional and structural or amplitude errors. This has been long recognized by practicing forecasters (cf., e.g., Tropical Cyclone track and intensity errors). Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors, most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error. The Forecast Error Decomposition (FED) method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field. The total error is then partitioned into three orthogonal components: (a) large scale positional, (b) large scale structural, and (c) small scale error variance. The use of FED is demonstrated over a month-long MSLP data set. As expected, positional errors are often characterized by dipole patterns related to the displacement of features, while structural errors appear with single extrema, indicative of magnitude problems. The most important result of this study is that over the test period, more than 50% of the total mean sea level pressure forecast error variance is associated with large scale positional error. The importance of positional error in forecasts of other variables and over different time periods remain to be explored.

Data-Driven MPC for Quadrotors

Aerodynamic forces render accurate high-speed trajectory tracking with quadrotors extremely challenging. These complex aerodynamic effects become a significant disturbance at high speeds, introducing large positional tracking errors, and are extremely difficult to model. To fly at high speeds, feedback control must be able to account for these aerodynamic effects in real-time. This necessitates a modeling procedure that is both accurate and efficient to evaluate. Therefore, we present an approach to model aerodynamic effects using Gaussian Processes, which we incorporate into a Model Predictive Controller to achieve efficient and precise real-time feedback control, leading to up to 70% reduction in trajectory tracking error at high speeds. We verify our method by extensive comparison to a state-of-the-art linear drag model in synthetic and real-world experiments at speeds of up to 14 m/s and accelerations beyond 4 g.

Retracted] Combined GNSS/SLAM‐Based High Accuracy Indoor Dynamic Target Localization Method

As the indoor dynamic target localization does not detect and repair the circumferential jump value in time, which leads to large position and attitude errors and low‐velocity stability, a combined Global Navigation Satellite System/Simultaneous Localization and Mapping‐ (GNSS/SLAM‐) based high‐precision indoor dynamic target localization method is proposed. The method uses Empirical Mode Decomposition (EMD) to decompose the noisy signal, obtains the noise energy as the dominant mode from the decomposed components, extracts the useful signal energy as the main dividing point, removes the high‐frequency signal, constructs the low‐frequency component to realize low‐pass filtering and denoising, selects a suitable threshold processing function to make the high‐frequency signal component retain the detailed signal effectively to realize high‐frequency component denoising, detects and fixes the circumferential jump of the observed data, and detects and fixes the circumferential jump of each frequency. The indoor dynamic target positioning method is established by combining GNSS/SLAM to achieve high accuracy target positioning. The experimental results show that the position and attitude errors are small, and the velocity is stable, which indicates that the position information is closer to the dynamic target, i.e., the target positioning is more accurate. To address the problems of scale drift and frequent initialization due to environmental factors in monocular vision SLAM, an Ultra Wideband (UWB)/vision fusion localization model that takes into account the scale factor is proposed, which can give full play to the complementary characteristics between UWB and vision; solve the problems of visual initialization, scale ambiguity, and absolute spatial reference; and improve UWB localization accuracy and localization frequency as well as reducing the number of base stations. The model can reliably achieve 0.2 m accuracy in indoor environments with sparse texture or frequent light changes.

High Precision Fabrication Method of Fresnel Diffractive Lenses on Ultrathin Membrane for Imaging Application

The microstructures on flexible membrane substrate prepared by the traditional methods may introduce large morphology and position errors, making it hard to achieve clear imaging. In this paper, we present a high precision fabrication method composed of two steps: unstressed fixation of the polyimide membrane and high precision fabrication of diffractive microstructures on the flexible membrane substrate. By using precise spin-coating technology, we get 25 μm thick polyimide membranes, whose coefficient of thermal expansion is almost zero. To prepare high precision microstructures, we propose an aerated membrane method, by which the flexible membrane substrate and the photoresist on it can attach tightly to the mask under the air pressure difference between the upper and lower surfaces of the membrane substrate during the contact lithography. The results show that the wave-front error obtained by this method is less than 1/30λ RMS (F# = 25@632.8 nm) at 400 mm aperture, indicating the microstructures have excellent morphologies and high position accuracy.

Low-Cost Automation for Gravity Compensation of Robotic Arm

During the Industry 4.0 era, the open source-based robotic arms control applications have been developed, in which the control algorithms apply for movement precision in the trajectory tracking paths based on direct or reverse kinematics. Therefore, small errors in the joint positions can summarize in large position errors of the end-effector in the industrial activities. Besides the change of the end-effector position for a given variation of the set-point in manipulator joint positions depends on the manipulator configuration. This research proposes a control based on Proportional Derivative (PD) Control with gravity compensation to show the robustness of this control scheme in the robotic arm’s industrial applications. The control algorithm is developed using a low-cost board like Raspberry Pi (RPI) where the Robot Operating System (ROS) is installed. The novelty of this approach is the development of new functions in ROS to make the PD control with gravity compensation in low-cost systems. This platform brings a fast exchange of information between the Kuka™ youBot robotic arm and a graphical user’s interface that allows a transparent interaction between them.

A compact ocean bottom electromagnetic receiver and seismometer

Abstract. Joint marine electromagnetic (EM) and seismic interpretations are widely used for offshore gas hydrate and petroleum exploration to produce improved estimates of lithology and fluids and to decrease the risk of low gas saturation. However, joint data acquisition is not commonly employed. Current marine EM data acquisition depends on an ocean bottom electromagnetic receiver (OBEM), and current seismic exploration methods use seismometers. Joint simultaneous data acquisition can decrease costs and improve efficiency, but conventional independent data receivers have several drawbacks, including a large size, high costs, position errors, and low operational efficiencies. To address these limitations, we developed a compact ocean bottom electromagnetic receiver and seismometer (OBEMS). Based on existing ocean bottom E-field receiver (OBE) specifications, including low noise levels, low power consumption, and low time drift errors, we integrated two induction coils for the magnetic sensor and a three-axis omnidirectional geophone for the seismic sensor to assemble an ultra-short baseline (USBL) transponder as the position sensor, which improved position accuracy and operational efficiency while reducing field data acquisition costs. The resulting OBEMS has a noise level of 0.1 nV m−1 rt−1 (Hz) at 1 Hz in the E-field, 0.1 pT rt−1 (Hz) at 1 Hz in the B-field, and a 30 d battery lifetime. This device also supports a Wi-Fi interface for the configuration of data acquisition parameters and data download. Offshore acquisition was performed to evaluate the system's field performance during offshore gas hydrate exploration. The OBEMS operated effectively throughout the operation and field testing. Therefore, the OBEMS can function as a low-cost, compact, and highly efficient joint data acquisition method.