Position Domain Research Articles

Insights into phosphoethanolamine cellulose synthesis and secretion across the Gram-negative cell envelope

Phosphoethanolamine (pEtN) cellulose is a naturally occurring modified cellulose produced by several Enterobacteriaceae. The minimal components of the E. coli cellulose synthase complex include the catalytically active BcsA enzyme, a hexameric semicircle of the periplasmic BcsB protein, and the outer membrane (OM)-integrated BcsC subunit containing periplasmic tetratricopeptide repeats (TPR). Additional subunits include BcsG, a membrane-anchored periplasmic pEtN transferase associated with BcsA, and BcsZ, a periplasmic cellulase of unknown biological function. While cellulose synthesis and translocation by BcsA are well described, little is known about its pEtN modification and translocation across the cell envelope. We show that the N-terminal cytosolic domain of BcsA positions three BcsG copies near the nascent cellulose polymer. Further, the semicircle’s terminal BcsB subunit tethers the N-terminus of a single BcsC protein in a trans-envelope secretion system. BcsC’s TPR motifs bind a putative cello-oligosaccharide near the entrance to its OM pore. Additionally, we show that only the hydrolytic activity of BcsZ but not the subunit itself is necessary for cellulose secretion, suggesting a secretion mechanism based on enzymatic removal of translocation incompetent cellulose. Lastly, protein engineering introduces cellulose pEtN modification in orthogonal cellulose biosynthetic systems. These findings advance our understanding of pEtN cellulose modification and secretion.

Segmentation of coronary artery based on discriminative frequency learning and coronary-geometric refinement

Coronary artery segmentation is crucial for physicians to identify and locate plaques and stenosis using coronary computed tomography angiography (CCTA). However, the low contrast of CCTA images and the intricate structures of coronary arteries make this task challenging. To address these difficulties, we propose a novel model, the DFS–PDS network. This network comprises two subnetworks: a discriminative frequency segment subnetwork (DFS) and a position domain scales subnetwork (PDS). DFS introduced a gated mechanism within the feed-forward network, leveraging the Joint Photographic Experts Group (JPEG) compression algorithm, to discriminatively determine which low- and high-frequency information of the features should be preserved for latent image segmentation. The PDS aims to learn the shape prototype by predicting the radius. Additionally, our model has the consistent ability to guarantee region and boundary features through boundary consistency loss. During training, both subnetworks are optimized jointly, and in the testing stage, the coarse segmentation and radius prediction are produced. A coronary-geometric refinement method refines the segmentation masks by leveraging the shape prior to being reconstructed from the radius map, reducing the difficulty of segmenting coronary artery structures from complex surrounding structures. The DFS–PDS network is compared with state-of-the-art (SOTA) methods on two coronary artery datasets to evaluate its performance. The experimental results demonstrate that the DFS–PDS network performs better than the SOTA models, including Vnet, nnUnet, DDT, CS2-Net, Unetr, and CAS-Net, in terms of Dice or connectivity evaluation metrics.

Testing the ionospheric model delay and uncertainty estimates with an uncombined navigation filter

In the last decade, new algorithmic positioning techniques have been developed for Global Navigation Satellite Systems (GNSS). These have brought a new focus on high accuracy applications which do not combine multiple frequencies to remove ionospheric errors (i.e. PPP-RTK, Fast-PPP). Not only do these algorithms focus on improvements in the position domain but also in acquiring the positioning solution as fast as possible. In this work, capabilities of different global ionospheric models are assessed, analyzing both the Ionospheric delay accuracy and the associated model uncertainty. Accurate model uncertainties are crucial for reducing the convergence time in uncombined filters, and to guarantee unbiased convergence in the first place. The assessment is done using an uncombined navigation filter with different ionospheric models: GPS ICA, IGS vTEC (vertical Total Electron Content) maps (IGSG, CODG and UQRG), two realizations of the ESA-UGI (Voxel and Multi-Layer), the Madrigal TEC, and the Spire Global vTEC maps. To quantify the model uncertainties without the use of a reference ionospheric model, global maps of an uncertainty inflation factor are computed to show the inflation required to produce optimal filter convergence. These maps demonstrate that some models are too optimistic in the reporting of their own uncertainty estimates, requiring an uncertainty factor up to 10 times the quoted value.

A Relook at Cerebral Palsy Beyond Motor Pathology: A Cross-Sectional Study of Sensory Processing Abilities

Background: Sensory processing refers to receiving, organizing, and interpreting sensory stimuli from the sensory system. Unlike other neurodevelopmental disorders, knowledge about the sensory processing abilities of children with cerebral palsy (CP) is lacking. Objective: To study the difference in sensory processing abilities of children with cerebral palsy in comparison to age matched typically developing children (TDC). Methods and Material: A cross-sectional analysis of sensory processing abilities of children with CP and TDC was performed from July 2018 through February 2020. The child sensory profile2 (CSP2) caregiver questionnaire was used to detect sensory processing differences (SPD) across nine sensory domains and four sensory processing patterns. A comparison was made between the two study groups as well as between the CP subtypes. Result: Around 226 children with CP and 58 TDC were screened. Finally, 150 children with CP and 50 TDC were enrolled. Probable SPD (>1SD) was observed in (121/150) 80.7% of children with CP compared to (13/50) 26% in TDC (p < 0.001). Definite SPD (>2SD) was seen in 40.7% (61/150) of children with CP vs. none in TDC (p < 0.001). The body position domain which tests the vestibular and proprioceptive processing was primarily affected in CP. Most children with CP fell under the “bystander” pattern suggesting poor registration of sensory stimuli. No significant difference in the pattern of sensory processing was observed between the CP subtypes. Prevalence of definite SPD positively correlated with the gross motor functional classification system level. Conclusion: Sensory processing abilities of children with CP differ significantly from TDC. Proprioceptive and vestibular sensory processing is primarily affected in CP.

Ionospheric corrections tailored to Galileo HAS: validation with single-epoch navigation

The Galileo high accuracy service (HAS) is a new capability of the European global navigation satellite system, currently providing satellite orbit and clock corrections and dispersive effects such as satellite instrumental biases for code and phase. In its full capability, Galileo HAS will also correct the ionospheric delay on a continental scale (initially over Europe). We analyze a real-time ionospheric correction system based on the fast precise point positioning (F-PPP), and its potential application to the Galileo HAS. The F-PPP ionospheric model is assessed through a 281-day campaign, confirming previously reported results, where the proof of concept was introduced. We introduce a novel real-time test that directly links the instantaneous position error with the error of the ionospheric corrections, a key point for a HAS. The test involved 15 GNSS receivers in Europe acting as user receivers at various latitudes, with distances to the nearest reference receivers ranging from tens to four hundred kilometers. In the position domain, the test results show that the 95th percentile of the instantaneous position error depends on the user-receiver distance, as expected, ranging in the horizontal and vertical components from 10 to 30 cm and from 20 to 50 cm, respectively. These figures not only meet Galileo HAS requirements but outperform them by achieving instantaneous positioning. Additionally, it is shown that formal errors of the ionospheric corrections, which are also transmitted, are typically at the decimeter level (1 sigma), protecting users against erroneous position by weighting its measurements in the navigation filter.

Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method

To enhance the positioning accuracy of standalone GNSS receivers in environments unable to provide precise ephemeris and clock offset, such as undeveloped forest areas that lack network communication and power supply, this study employed the Time Difference Carrier Phase (TDCP) technology to improve the positioning accuracy of Standard Point Positioning (SPP), where the Least-Squares (LS) and the extended Multi-Epoch Least Squares (MELS) method were applied in the position domain filtering for a single GNSS receiver and compare its performance with the existing observation domain filtering method. Firstly, the simulated data sets with various positioning accuracies were used to verify the effectiveness and convergence of the LS filtering methods. The results indicate that the LS filtering method produces a lower root mean square (RMS) error than the original strategy. Secondly, this study uses two kinematic GNSS data sets to evaluate the performance of the observation and position domain filtering, with an emphasis on the MELS method. The numerical experiment results show that the position domain LS filtering method outperforms the other two methods. The open environment experiments result shows that the positioning domain filtering method achieved positioning accuracies of 0.202 m, 0.843 m, and 2.036 m in the E, N, and U directions, respectively, with improvements of 68.0%, 21.6%, and 24.0%, compared to the original algorithm which achieved positioning accuracies of 0.631 m, 1.076 m, and 2.680 m. It also achieved improvements of 24.0%, 4.0%, and 18.3%, respectively, compared to the observation domain filtering method with positioning accuracies of 0.353 m, 0.886 m, and 2.526 m. The forest scenes experiments result shows that the positioning domain filtering method achieved positioning accuracies of 1.308 m, 1.375 m, and 2.133 m in the E, N, and U directions, respectively, with improvements of 42.4%, 36.2%, and 27.6%, compared to original algorithm which achieved positioning accuracies of 1.863 m, 1.873 m, and 2.722 m, and also achieved improvements of 27.0%, 19.4% and 10.6%, respectively, comparing to observation domain filtering method with positioning accuracies of 1.661 m, 1.642 m and 2.359 m. Moreover, the examination of the LS method results based on different epochs reveals that the filtering accuracy increases as more epochs are incorporated into the position domain integration and the enhancement value reaches a few millimeters.

Statistical evaluation of tongue capability with visual feedback

BackgroundAnalysis of tongue movement would benefit from a reference showcasing healthy tongue capability. We aimed to develop a reference of tongue capability and evaluated the role of visual feedback on the expression of movement.MethodsUsing a wireless tracking intraoral wearable device, we composed probability distributions of the tongue tip as subjects were asked to explore the entire sensing surface area. Half of the 32 subjects received live visual feedback of the location of the center of the tongue tip contact.ResultsWe observed that the visual feedback group was 51.0% more consistent with each other in the position domain, explored 21.5% more sensing surface area, and was 50.7% more uniformly distributed. We found less consistent results when we evaluated velocity and acceleration.ConclusionVisual feedback best established a healthy capability reference which can be used for designing new interfaces, quantifying tongue ability, developing new diagnostic and rehabilitation techniques, and studying underlying mechanisms of tongue motor control.

BDS-3 RTK/UWB semi-tightly coupled integrated positioning system in harsh environments

Real-time kinematic (RTK) positioning is a commonly used technique in modern industry, which is limited by problems such as signal occlusion, attenuation, and multipath, especially in complex urban canyons. To maintain the consistency of centimeter-level accuracy, we adopt the ultra-wideband (UWB) enhanced BDS-3 RTK positioning algorithm. This paper proposed a semi-tightly coupled (STC) BDS-3 RTK/UWB integration positioning model. This model realizes the UWB and BDS-3 complement each other and integrate information in the position domain. Besides, height constraint is imposed on UWB positioning to mitigate the effect of poor positioning of UWB in height components. To verify the effectiveness of the above algorithm, we have compared and analyzed the positioning performance of the STC BDS-3 RTK/UWB integration model and single BDS-3 RTK model in different occlusion environments. The positioning performance of static and kinematic of BDS-3 RTK/UWB STC based on different number of UWB anchors is further analyzed. The real-world experiment results show that the positioning accuracy of the proposed method can reach centimeter-level. Moreover, the proposed model can obtain more accurate positioning results than those of using single system, and it shows more obvious advantages, especially in the occlusion environment. In the occlusion environment, the root means square error in the east, north, and up directions is improved from (0.629 m, 0.325 m, 1.160 m) of the BDS-3-only to (0.075 m, 0.074 m, 0.029 m). This study can provide a reference for the future development of high-precision, real-time, continuous positioning, navigation, and timing in complex urban environments.

Synthetic Meta-Signal Observations: The Beidou Case.

A Global Navigation Satellite System (GNSS) meta-signal is obtained when two or more side-band components from different frequencies are jointly processed as a single entity. This requires advanced signal processing techniques able to cope with the subcarrier, generated by the interaction of the side-band components, and handle possibly multi-peaked ambiguous correlation functions. An alternative approach to meta-signal processing is to reconstruct meta-signal observations using side-band measurements. Meta-signal high-accuracy pseudoranges can be reconstructed from the side-band code and carrier observations. The success of the reconstruction depends on several factors, including the frequency separation of the side-band components and the presence of measurement biases. The Beidou Navigation Satellite System (BDS), with its second- and third-generation signals, provides a wide range of components with various frequency separations. In this paper, we experimentally investigate the performance and limitations of the measurement reconstruction approach using Beidou observations. When the B1I and B1C components are considered, their reduced frequency separation leads to unambiguous measurements fully exploiting the potential of meta-signals. For larger frequency separations, jumps and discontinuities are observed in the position domain, which is a major limiting factor of this type of approach.

A revised BDS Klobuchar model with a non-symmetrical processing strategy and a high-latitude amplitude constraint

The BeiDou Navigation Satellite System (BDS) broadcasts the BDS Klobuchar model (BDSK) for ionospheric delay correction in both BDS-2 regional service phase and BDS-3 global service phase. Unlike the GPS Klobuchar model, the BDSK model adopts a simple “symmetrical strategy” due to the limited ionospheric input data within China, which results in inaccurate total electron content (TEC) estimation in the southern hemisphere. Besides, the “high latitude anomaly” occurs in the BDSK model due to the lack of latitude limitation strategy, which deteriorates the accuracy of the BDSK model in the southern hemisphere further. To solve this problem, we propose a revised BDS Klobuchar model (RBDK), in which the “non-symmetrical strategy” is adopted in the southern hemisphere and an amplitude constraint is introduced at high latitudes. For comparison, the fitted BDS Klobuchar model (BDFK) that is estimated using the same ionospheric input data and constraint algorithm for the RBDK model but with the “symmetrical strategy” is also included. First, we conduct a comparative analysis of the time series of the eight parameters for the BDSK, BDFK and RBDK models in 2021. The parameters of the BDFK and RBDK models are more stable, and there will be no abnormal jumping phenomenon that occurs in the BDSK model. Then, the performance of the broadcast BDSK model, the fitted BDFK model and the proposed RBDK model during the whole year of 2021 are evaluated by comparing them with the high-precision global ionospheric map (GIM) of the International GNSS Service (IGS). In the southern hemisphere, the root mean square (RMS) values of the BDSK and BDFK are 6.5 and 5.8 TEC unit (TECU), respectively, while that of the RBDK is 4.6 TECU, showing improvements of 29% and 21%, respectively. At high latitudes in the northern hemisphere, the RMS value of the BDSK is 7.6 TECU, while they are only 3.7 TECU for the RBDK and BDFK when the amplitude constraint is introduced. Final, we select 16 IGS stations to validate the performance of the BDSK, BDFK and RBDK models in the standard point positioning (SPP) domain in 2021. The average SPP accuracy of the BDSK is 8.88 m at 6 high-latitude stations, while they are 5.48 m for the RBDK and BDFK, showing significant improvement of 38%. Excluding the high-latitude stations, the SPP accuracy of the RBDK outperforms the BDFK and BDSK by 14% and 18%, respectively, at stations in the southern hemisphere. As expected, the proposed RBDK model shows significantly better performance in the southern hemisphere and at high latitudes in contrast to the broadcast BDSK model.

Impact of using type mean vs. individual receiver antenna PCC in multi-GNSS PPP

In the paper, the differences between position components obtained using the precise point positioning (PPP) technique with individual and type mean PCC models were investigated. Daily GNSS observations from ten selected European Permanent Network (EPN) stations were used in the study. Eight different combinations of GPS, Galileo, BeiDou and GLONASS systems observations were proposed for analyses. The observation processing was done using the open-source software package GAMP. The results show that differences in the calibration models propagate to the position domain. Position offsets, resulting from the use of individual calibrations instead of the type mean, reach up to 10 mm in the vertical component while are generally keeping below 2 mm in the horizontal ones. The analysis also shows that adapting GPS PCCs to other GNSS (e.g. Galileo or BeiDou) signals does not significantly increase achieved position component differences.

Tracking control for a position-dependent periodic signal in a variable-speed rotational system

This paper concerns the problem of tracking a position-related periodic signal for an uncertain nonlinear rotational system. Applying the additive-state-decomposition (ASD) technique decomposes the original uncertain nonlinear system into two subsystems: A primary linear time-invariant system that performs spatial repetitive control, and a secondary nonlinear system that ensures robust stability of the whole system with respect to unknown nonlinear dynamics and exogenous disturbances. This method has four features: (i) The ASD technique provides an effective way to deal with the coupling between the spatial periodicity and other factors (temporal and state-dependent uncertainties and disturbances) in a rotational system. This technique ensures satisfactory tracking and disturbance rejection performance; (ii) A real-time digital implementation of a spatial repetitive controller enables repetitive control with an exact period in the position. This ensures the synchronous execution of repetitive control in the position domain and robust stability in the time domain; (iii) The method of accurate phase compensation in a spatial repetitive controller leads to a significant improvement in steady-state tracking performance; and (iv) An extended-state observer precisely estimates the overall effect of disturbances. Integrating a disturbance estimate into a control input effectively compensates for disturbances. Stability criteria and design algorithms are presented in detail. Finally, experiments of a rotational speed-control system demonstrate the effectiveness of this method.

V-RTK: A velocity-constrained RTK algorithm to improve position accuracy of low-cost receiver in urban environments

In harsh signal environments, such as urban canyon, overpass and so on, global navigation satellite system (GNSS) navigation will be influenced by insufficient satellites, signal degradation, and bad quality which lead to the lack of redundancy and re-initialization of the ambiguities, especially for the low cost real-time kinematic (RTK) equipment. To address these issues, an velocity-constrained RTK method (V-RTK) for low-cost receiver is proposed. Instead of being initialized with the standard point positioning (SPP) result, the predicted position parameters are calculated with the velocity obtained from GNSS observation or other sensors, so that the historical filter message can be delivered to the next epoch both from the ambiguity domain and the position domain to maintain the accuracy of historical epochs. The performance of the proposed method has been assessed with two kinematic vehicular datasets collected in an urban environment, which lasts about 3 h. The results manifest that the positioning trajectory is smoother and location jumps are significantly reduced after applying the velocity constraint. And the accuracy of each dimension is improved by 10%∼40%. Besides, an in-field experiment is conducted to compare the proposed method with the PVA method, which shows a better performance in our proposed method for making full use of observations.

Intra-frame motion deterioration effects and deep-learning-based compensation in MR-guided radiotherapy.

Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra-frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. The aim of this work is to investigate intra-frame motion deterioration effects in MR-guided radiotherapy (MRgRT) by simulating the motion-corrupted image acquisition, and to explore the feasibility of deep-learning-based compensation approaches, relying on the intra-frame motion information which is spatially and temporally encoded in the raw data (k-space). An intra-frame motion model was defined to simulate motion-corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra-frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra-frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U-Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss-L1 , NNFloss-L1 and NNL2-Loss were trained to extract information from the motion-corrupted image and used to estimate the ground truth final-position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean-squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical-flow image registration. Image degradation caused by intra-frame motion was observed: for a linearly and fully acquired Cartesian readout k-space trajectory, intra-frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion-corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95 ) dropped from 4.8mm to 2.1mm. Different models showed slight performance variations across different intra-frame motion amplitude categories: NNImgLoss-L1 excelled for small/medium amplitudes, whereas NNFloss-L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion-corrupted image highlighted the major contribution of the later acquired k-space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. Our results demonstrate the deep-learning-based approaches have the potential to compensate for intra-frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k-space components.

Robot trajectory planning for autonomous 3D reconstruction of cockpit in aircraft final assembly testing

The trend towards automation and intelligence in aircraft final assembly testing has led to a new demand for autonomous perception of unknown cockpit operation scenes in robotic collaborative airborne system testing. To address this demand, a robotic automated 3D reconstruction cell which enables to autonomously plan the robot end-camera's trajectory is developed for image acquisition and 3D modeling of the cockpit operation scene. A continuous viewpoint path planning algorithm is proposed that incorporates both 3D reconstruction quality and robot path quality into optimization process. Smoothness metrics for viewpoint position paths and orientation paths are introduced together for the first time in 3D reconstruction. To ensure safe and effective movement, two spatial constraints, Domain of View Admissible Position (DVAP) and Domain of View Admissible Orientation (DVAO), are implemented to account for robot reachability and collision avoidance. By using diffeomorphism mapping, the orientation path is transformed into 3D, consistent with the position path. Both orientation and position paths can be optimized in a unified framework to maximize the gain of reconstruction quality and path smoothness within DVAP and DVAO. The reconstruction cell is capable of automatic data acquisition and fine scene modeling, using the generated robot C-space trajectory. Simulation and physical scene experiments have confirmed the effectiveness of the proposed method to achieve high-precision 3D reconstruction while optimizing robot motion quality.

GNSS integrity risk evaluation in the position domain based on the generalized Pareto distribution

Integrity monitoring of global navigation satellite systems (GNSSs) is designed to protect against extremely rare hazardous events, characterized by an integrity risk with a very low probability. The traditional integrity risk evaluation is restricted simultaneously by non-Gaussian measurement errors and impractical time consumption. Based on extreme value theory, a generalized Pareto distribution (GPD)-based integrity risk evaluation method in the position domain is proposed to estimate the upper bound of the integrity risk. In order to account for the GPD modeling error and estimation error, conservatism of the proposed GPD-based integrity risk evaluation is obtained by imposing model-driven and data-driven overbounding. Simulation results from four typical heavy-tailed distributions have shown that conservative and tight bound integrity risk results can be achieved. Furthermore, real-world European Geostationary Navigation Overlay Service measurements experiment has shown that the integrity risk evaluation resulting from the proposed method is at least one order less than the traditional evaluation method, which is consistent with official publications.

R-AP's position-domain model for an RO-receiver and AP placement strategy for an uninterrupted link between the robotic arm and AP in the VLC network.

In the visible light communication (VLC) network, the optical link between the robotic arm and the access point (AP) is easily interrupted due to the random orientation of the receiver on the robotic arm. First, based on the VLC channel model, a position-domain model of reliable AP (R-AP) for a random-orientation receiver (RO-receiver) is proposed. The channel gain of the VLC link between the receiver and the R-AP is non-zero. The tilt-angle range of the RO-receiver is [0,π]. Given the field of view (FOV) angle and the orientation of the receiver, the R-AP's position domain of the receiver can be obtained by this model. Then, based on the R-AP's position-domain model for the RO-receiver, a novel AP placement strategy is proposed. Under this AP placement strategy, the number of R-APs for the RO-receiver is not less than 1, thereby effectively avoiding link interruption caused by the random orientation of receivers. Finally, it is proved by the Monte Carlo method that, under the AP placement strategy proposed in this paper, the VLC link of the receiver on the robotic arm can remain uninterrupted during the movement of the robotic arm.

Neustrelitz Total Electron Content Model for Galileo Performance: A Position Domain Analysis

Ionospheric error is one of the largest errors affecting global navigation satellite system (GNSS) users in open-sky conditions. This error can be mitigated using different approaches including dual-frequency measurements and corrections from augmentation systems. Although the adoption of multi-frequency devices has increased in recent years, most GNSS devices are still single-frequency standalone receivers. For these devices, the most used approach to correct ionospheric delays is to rely on a model. Recently, the empirical model Neustrelitz Total Electron Content Model for Galileo (NTCM-G) has been proposed as an alternative to Klobuchar and NeQuick-G (currently adopted by GPS and Galileo, respectively). While the latter outperforms the Klobuchar model, it requires a significantly higher computational load, which can limit its exploitation in some market segments. NTCM-G has a performance close to that of NeQuick-G and it shares with Klobuchar the limited computation load; the adoption of this model is emerging as a trade-off between performance and complexity. The performance of the three algorithms is assessed in the position domain using data for different geomagnetic locations and different solar activities and their execution time is also analysed. From the test results, it has emerged that in low- and medium-solar-activity conditions, NTCM-G provides slightly better performance, while NeQuick-G has better performance with intense solar activity. The NTCM-G computational load is significantly lower with respect to that of NeQuick-G and is comparable with that of Klobuchar.

Wavelet and Neural Network-Based Multipath Detection for Precise Positioning Systems

Multipath errors are significantly challenging in radio navigation systems. In particular, multipath errors in indoor environments cause significant errors in the position domain because not only the building materials that surround the environment but also all objects inside the building can reflect the navigation signals. Multipath errors in outdoor environments, such as in global navigation satellite system (GNSS) signal applications, have been widely studied for precise positioning. However, multipath studies for indoor applications have rarely been conducted because of the complicated environment and the many objects made of various materials in small areas. In this study, multipath mitigation methods using a shallow neural network and a transfer learning-based deep neural network were respectively considered to overcome the complexity caused by the reflected signals in indoor environments. These methods classify each measurement according to whether the measurement exhibits a severe multipath error. Carrier-phase measurements broadcasted from the transmitter were used for the wavelet transform, and the magnitude values after the transform were used for neural network-based learning. Shallow and deep networks attain approximately 87.1% and 85.6% detection accuracies, respectively, and the positioning error can be reduced by 10.4% and 9.4%, respectively, after multipath mitigation.

Gaussian Process Repetitive Control With Application to an Industrial Substrate Carrier System With Spatial Disturbances

Repetitive control (RC) can perfectly attenuate disturbances that are periodic in the time domain. The aim of this article is to develop an RC approach that compensates for disturbances that are time-domain nonperiodic but are repeating in the position domain. The developed position-domain buffer consists of a Gaussian process (GP), which is learned using appropriate dynamic filters and nonequidistant data. This approach estimates position-domain disturbances resulting in perfect compensation. The method is successfully applied to a substrate carrier system, demonstrating performance robustness against time-domain nonperiodic disturbances that are amplified by traditional RC.