Ambiguity Resolution Research Articles

Satellite orientation modelling with quaternions and its impact on BDS-3 PPP-AR

Satellite orientation is a fundamental component of processing high-precision data from the Global Navigation Satellite System (GNSS). However, insufficient handling of satellite orientation may substantially affect the quality of the precise point positioning (PPP) solutions. This research investigates the influence of satellite orientation using satellite attitude quaternions on the third-generation BeiDou Navigation Satellite System (BDS-3) PPP. To illustrate the impact of satellite attitude, both float and PPP with integer ambiguity resolution (PPP-AR) were undertaken using observations from a multi-GNSS Experiment (MGEX) network. To fully evaluate the effect of the quaternions on PPP, a comprehensive comparison between BDS-3 and Galileo constellations was conducted using nominal attitude and quaternions. The findings indicate a significant performance difference between BDS-3 and Galileo satellites in phase observations after resolving integer ambiguities, with Galileo consistently demonstrating superior phase residuals across various receivers. When it comes to positioning accuracy, the utilisation of quaternions improves coordinate precision across both constellations. Furthermore, the quaternions enhance the stability of time series for the positioning biases and observational residuals.

UPDs estimation and ambiguity resolution performance evaluation of LEO navigation system

Low-Earth orbit (LEO) satellites have fast motion and thus provide rapid geometric change relation to the ground stations in a short period of time, which are expected to improve the positioning performance. In this study, a LEO constellation of 160 LEO satellites was simulated and simulated LEO observation data at global, European, and American stations were used to estimate the uncalibrated phase delays (UPDs) and achieve static and kinematic precise point positioning (PPP) ambiguity resolution (AR). The quality of the estimated UPD products were evaluated. More than 89 % and 96 % of the wide- and narrow-lane UPDs had a posteriori residual of less than 0.15 and 0.25 cycles, respectively. In static PPP, all ambiguity fixing rates were larger than 63 %. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 8 %, 6 % and 7 %, respectively; the average root-mean-square errors (RMSEs) in the east, north, and up components at the global, European and American stations were decreased by (22 %, 20 %, 15 %), (36 %, 34 %, 28 %) and (44 %, 31 %, 29 %), respectively. In kinematic PPP, all ambiguity fixing rates were larger than 40 %. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 4 %, 19 % and 14 %, respectively; the average RMSEs in the east, north, and up components at the global, European and American stations were decreased by (10 %, 10 %, 7 %), (27 %, 22 %, 11 %) and (32 %, 20 %, 10 %), respectively. In both static and kinematic PPP, AR accelerated the convergence time and improved the positioning accuracy.

The integration of Natural Language Processing (NLP) in Human-Robot Interaction (HRI) represents a significant advancement towards achieving more natural and effective communication between humans and robots. This research explores the application of state-of-the-art NLP techniques to enhance HRI, focusing on improving robots' abilities to understand and generate human language. Key components of our approach include advanced speech recognition, natural language

The integration of Natural Language Processing (NLP) in Human-Robot Interaction (HRI) represents a significant advancement towards achieving more natural and effective communication between humans and robots. This research explores the application of state-of-the-art NLP techniques to enhance HRI, focusing on improving robots' abilities to understand and generate human language. Key components of our approach include advanced speech recognition, natural language understanding (NLU), dialogue management, and natural language generation (NLG). We designed and implemented an HRI system that leverages models such as BERT for language understanding and GPT-3 for generating contextually appropriate responses. Our methodology involves integrating these NLP models with a robotics platform, ensuring real-time interaction capabilities while maintaining a high level of accuracy and context awareness. The system was evaluated through a series of user studies, measuring performance metrics such as accuracy, latency, and user satisfaction. Results indicate that our NLP-enhanced HRI system significantly improves the quality of interactions, demonstrating superior understanding and responsiveness compared to traditional systems. This paper discusses the implementation challenges, including computational constraints and ambiguity resolution, and provides insights into user feedback and system performance. Future work will focus on enhancing context management, exploring multimodal interaction, and addressing ethical considerations in deploying advanced HRI systems. Our findings underscore the potential of NLP to transform human-robot communication, paving the way for more intuitive and effective robotic assistants in various domains. Keywords: Human-Robot Interaction (HRI), Natural Language Processing (NLP), Conversational AI, Speech Recognition, Natural Language Understanding (NLU), Natural Language Generation (NLG), Multimodal Interaction, Dialogue Systems, Context Awareness, Emotion Recognition, Machine Learning in HRI, Personalized Interaction, User Experience (UX) in HRI, Human-Centered Design, Collaborative Robots (Cobots)

Elevating performance in BDS-3 Precise Point Positioning Ambiguity Resolution through LEO augmented constellations

With the help of constellations combined BDS-3 (BeiDou Navigation Satellite System) and LEO (Low Earth Orbit), we aim to improve the performances of PPP (Precise Point Positioning) and PPP Ambiguity Resolution (PPP-AR). First, LEO constellations with 66, 96, 156, 192, 270, and 288 satellites are designed to establish six LEO augmented constellations. Based on four challenge scenarios, PPP of six LEO augmented constellations are performed. Even in the strip-shaped obstruction area losing more than 60% of observations, the positioning accuracies of static PPP are improved by (1.0%, 5.7%, 0.2%) for BDS+66LEO, and (14.9%, 33.7%, 7.5%) for BDS+288LEO in the east, north, and up components. The positioning accuracy and convergence time improve quickly as the number of LEO satellites increased from 66 to 156, and slowdown from 192 to 288. Two constellations, BDS+156LEO and BDS+192LEO, are considered to strike the balance of acceptable PPP performance and constellation load. Then, the performance of PPP-AR is realized with the estimated UPD (Uncalibrated Phase Delay). The correlations among geodetic parameters and ambiguities deriving from the post-fit variance–covariance matrix are analyzed to illustrate the enhancement of LEO augmented constellations. Under the condition of 20°elevation angle occlusion with azimuth ranging from 0 to 360°, BDS+156LEO can achieve an ambiguity fix rate of up to 98.37% with a faster mean TTFF (Time To First Fix) of 12.76 min, and the positioning accuracies in the east, north, and up components are improved by 28.2%, 21.4%, and 15.1% respectively, compared with BDS-3. BDS+192LEO realized an 98.42% ambiguity fixing rate, and improved BDS-3 by 31.4%, 26.7% and 11.5% with its TTFF been 12.25 min.

All-frequency IGS phase clock/bias product combination to improve PPP ambiguity resolution

All-frequency IGS phase clock/bias product combination to improve PPP ambiguity resolution

Enhanced prosody adds to morpho-syntactic cues in the interpretation of structural ambiguities in German

ABSTRACT This study investigated the effects of syntactically marked and enhanced prosody on local ambiguity resolution in German SVO and OVS sentences. In a visual-world experiment, thirty younger and thirty elderly healthy participants performed a sentence-picture matching task. Response accuracy, reaction times and fixations proportions to the target picture were analysed using linear mixed models. We found no support for beneficial effects of syntactically marked prosody, however, results suggested a facilitative role of enhanced prosodic cues (i.e. increased f0 maximum) prior to the point of disambiguation in SVO structures, as well as the beneficial effects of enhanced prosody adding to morpho-syntactic cues in OVS structures. Both age groups showed comparable cue use but inter-individual variability in prosodic cue processing. Overall, our study replicates and extends previous findings demonstrating the importance of examining variability in prosodic cue processing in future research.

Resolution of RHCE Haplotype Ambiguities in Transfusion Settings.

Red blood cell (RBC) transfusion, limited by patient alloimmunization, demands accurate blood group typing. The Rh system requires specific attention due to the limitations of serological phenotyping methods. Although these have been compensated for by molecular biology solutions, some RhCE ambiguities remain unresolved. The RHCE mRNA length is compatible with full-length analysis and haplotype discrimination, but the RHCE mRNA analyses reported so far are based on reticulocyte isolation and molecular biology protocols that are fastidious to implement in a routine context. We aim to present the most efficient reticulocyte isolation method, combined with an RT-PCR sequencing protocol that embraces the phasing of all haplotype configurations and identification of any allele. Two protocols were tested for reticulocyte isolation based either on their size/density properties or on their specific antigenicity. We show that the reticulocyte sorting method by antigen specificity from EDTA blood samples collected up to 48 h before processing is the most efficient and that the combination of an RHCE-specific RT-PCR followed by RHCE allele-specific sequencing enables analysis of cDNA RHCE haplotypes. All samples analyzed show full concordance between RHCE phenotype and haplotype sequencing. Two samples from the immunohematology laboratory with ambiguous results were successfully analyzed and resolved, one of them displaying a novel RHCE allele (RHCE*03 c.340C>T).

Risk overbounding for ionospheric gradient monitor using geometry-free double differenced carrier phase measurements

Ionosphere anomaly can cause large spatial gradients in the double-differenced carrier phase (DDCP) measurements. Taking advantage of this characteristic, the ionospheric gradient monitor (IGM) is designed with multiple ground reference stations to detect threatening ionospheric gradients for safety of life applications. An optimal IGM should be most sensitive to ionospheric gradients and least sensitive to other errors. However, current IGM suffers from the influence of tropospheric error, which can degrade monitor performance under extreme weather conditions by causing risks of false alarms and missed detections. To address this issue, an alternative IGM is proposed using the geometry-free combination of DDCP as the test statistic. Ambiguity resolution procedure is designed for estimating the ambiguity term in the test statistic. The risk induced by the wrong ambiguity fix and tropospheric error are analyzed and bounded with required averaging period and minimum baseline length. The results show that the proposed theoretical IGM is capable of achieving probability of false alarm of 10–8 and probability of missed detection of 10–6 with a filtering period of 612 s and baseline length of 371.7 m or a filtering period of 544 s and baseline length of 384.4 m. The experimental results using data collected from Hong Kong Satellite Positioning Reference Station Network demonstrate that the performance of the proposed theoretical IGM is comparable to that of the existing detection algorithm.

An improved GNSS ambiguity best integer equivariant estimation method with Laplacian distribution for urban low-cost RTK positioning

The integer least squares (ILS) estimation is commonly used for carrier phase ambiguity resolution (AR). More recently, the best integer equivariant (BIE) estimation has also attracted an attention for complex application scenarios, which exhibits higher reliability by a weighted fusion of integer candidates. However, traditional BIE estimation with Gaussian distribution (GBIE) faces challenges in fully utilizing the advantages of BIE for urban low-cost positioning, mainly due to the presence of outliers and unmodeled errors. To this end, an improved BIE estimation method with Laplacian distribution (LBIE) is proposed, and several key issues are discussed, including the weight function of LBIE, determination of the candidates included based on the OIA test, and derivation of the variance of LBIE solutions for reliability evaluation. The results show that the proposed LBIE method has the positioning accuracy similar to the BIE using multivariate t-distribution (TBIE), and significantly outperforms the ILS-PAR and GBIE methods. In an urban expressway test with a Huawei Mate40 smartphone, the LBIE method has positioning errors of less than 0.5 m in three directions and obtains over 50% improvements compared to the ILS-PAR and GBIE methods. In an urban canyon test with a low-cost receiver STA8100 produced by STMicroelectronics, the positioning accuracy of LBIE in three directions is 0.112 m, 0.107 m, and 0.252 m, respectively, with improvements of 17.6%, 27.2%, and 26.1% compared to GBIE, and 23.3%, 28.2%, and 30.6% compared to ILS-PAR. Moreover, its computational time increases by 30–40% compared to ILS-PAR and is approximately half of that using TBIE.

GNSS carrier phase common-view precision time transfer using the ionospheric-weighted single-differenced model

ABSTRACT High-precision global navigation satellite system (GNSS) time transfer depends on the carrier phase observations, and ionospheric delay affects rapid integer ambiguity resolution. Therefore, this work applies the ionospheric-weighted single-differenced model. Experimental results indicate that for a time-link of 67.9 km, the time transfer accuracy of different single-system is better than 0.03 ns. Compared with single-system, multi-system demonstrates 8% to 14% improvement in accuracy and improvement of no more than 31% in frequency stability. Additionally, compared with the case without ionospheric constraint, imposing ionospheric constraints can improve performance of time transfer, but the gain decreases as the time-link length increases.

Exploring variation in English and Italian relative clause attachment: The role of coordination

This study investigates the effect of coordination on the resolution of relative clause attachment ambiguity in English and Italian. We also examined the interplay of RC length and DP positions on attachment preferences in coordinate structures, conducting a partial replication of previous results on English (Hemforth et al. 2015). In two offline force-choice tasks, English speakers favored local attachment, while Italian speakers showed a strong preference for non-local attachment across all conditions. This pattern aligns with established variation across the two languages, but interestingly deviates from earlier reports showing the effects of RC-Head type, RC length, and DP position on attachment decisions. Our findings thus suggest that further attention needs to be paid to the complex interaction of different, potentially understudied, structural factors when investigating disambiguation mechanisms across languages.

What prosody does when morphosyntax is absent: The case of Korean relative clauses

This study investigates the role of prosodic information in linguistic interpretation in the absence of an explicit linguistic marker to resolve ambiguity. We particularly focus on the impact of prosody on comprehension of restrictive relative clauses (RRC) and non-restrictive relative clauses (NRC) in Korean, a language that lacks morphosyntactic or orthographic markers that distinguish between RRC and NRC. We hypothesize that narrow focus prosody may be associated with RRC, while broad focus prosody with NRC, which we test in two experiments through a picture selection task (Experiment 1) and an audio selection task (Experiment 2). Results showed that Korean listeners associated narrow focus prosody more often with RRC-biased pictures than NRC-biased pictures, suggesting that prosodic information has an impact on the resolution of syntactic ambiguity in the absence of any explicit linguistic marker. Further investigation suggests that there is variation in the impact of prosody across individuals and their sensitivity to prosody seems to be affected differently depending on the type of task.

An improved wide-lane ambiguity resolution method for kinematic smartphone positioning

Abstract The release of GNSS raw data on Android smartphones provides the potential for high-precision smartphone positioning using multi-constellation and multi-frequency signals. However, severe multipath and low observation quality in kinematic environments make double-differenced uncombined ambiguities difficult to resolve reliably. To address this, the paper proposes an improved wide-lane (WL) integer ambiguity resolution (IAR) method that combines integer rounding and the Least-Square AMBiguity Decorrelation Adjustment (LAMBDA) methods. The proposed method achieved fix rates of 57% to 70% in challenging environments, with an average improvement of 7 · 7% in horizontal positioning accuracy compared to the float solution. The traditional partial integer rounding method only improved accuracy by 1 · 1%.

Crowdsourcing RTK: a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution (AR) in precise positioning. However, traditional real-time precise positioning frameworks (i.e., NRTK and PPP-RTK) depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network. This results in limited public appeal. With the mass production of autonomous driving vehicles, more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps. In this study, we propose a new GNSS positioning framework that relies on dual base stations, massive vehicle GNSS data, and crowdsourced atmospheric delay correction maps (CAM). The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way. Specifically, the map consists of between-station single-differenced ionospheric and tropospheric delays. We introduce the whole framework of CAM initialization for individual vehicles, on-cloud CAM maintenance, and CAM-augmented user-end positioning. The map data are collected and preprocessed in vehicles. Then, the crowdsourced data are uploaded to a cloud server. The massive data from multiple vehicles are merged in the cloud to update the CAM in time. Finally, the CAM will augment the user positioning performance. This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other. We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales. We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Real-Time Estimation of BDS-3 Satellite Clock Offset with Ambiguity Resolution Using B1C/B2a Signals

The third generation of the BeiDou navigation satellite system (BDS-3) can transmit five-frequency signals. The real-time satellite clock offset of BDS-3 is typically generated utilizing the B1I/B3I combination with the ambiguity-float solutions. By conducting the ambiguity resolution (AR), the reliability of the satellite clock offset can be improved. However, the performance of BDS-3 ambiguity-fixed real-time satellite clock offset with B1C/B2a signals remains unknown and unrevealed. In this contribution, the performance of the BDS-3 ambiguity-fixed satellite clock offset with the new B1C/B2a signals is investigated. One week of observation data from 85 stations was used to perform ambiguity-fixed satellite clock offset estimation. For B1I/B3I and B1C/B2a signals, the wide-lane (WL) uncalibrated phase delay (UPD) on the satellite end is fairly stable for one day, while the narrow-lane (NL) UPD standard deviation (STD) amounts to 0.122 and 0.081 cycles, respectively. The mean ambiguity fixing rate is 80.7% and 78.0% for these two signal combinations, and the time to first fix (TTFF) for the B1C/B2a signals is remarkably shorter than that of the B1I/B3I signals. The STDs of the ambiguity-float and -fixed satellite clock offsets are 0.033 and 0.026 ns, respectively, for the B1I/B3I combination, and it is reduced to 0.024 and 0.023 ns for B1C/B2a signals, respectively. Using the estimated UPD and clock offset products, the positioning performance of the kinematic Precise Point Positioning (PPP)-AR results amounts to 1.56, 1.23, and 4.46 cm in the east, north, and up directions for B1I/B3I signals, respectively. It is improved to 1.36, 1.16, and 4.25 cm using the products estimated with the B1C/B2a signals, with improvements of 12.8%, 5.7%, and 4.7% in three directions, respectively. The experiments showed that the performances of the ambiguity-fixed satellite clock offsets and the PPP-AR results using B1C/B2a signals are better than those of B1I/B3I.

Learning capabilities to resolve tilt-translation ambiguity in goldfish.

Spatial orientation refers to the perception of relative location and self-motion in space. The accurate formation of spatial orientation is essential for animals to survive and interact safely with their environment. The formation of spatial orientation involves the integration of sensory inputs from the vestibular, visual, and proprioceptive systems. Vestibular organs function as specialized head motion sensors, providing information regarding angular velocity and linear acceleration via the semicircular canals and otoliths, respectively. However, because forces arising from the linear acceleration (translation) and inclination relative to the gravitational axis (tilt) are equivalent, they are indistinguishable by accelerometers, including otoliths. This is commonly referred to as the tilt - translation ambiguity, which can occasionally lead to the misinterpretation of translation as a tilt. The major theoretical frameworks addressing this issue have proposed that the interpretation of tilt versus translation may be contingent on an animal's previous experiences of motion. However, empirical confirmation of this hypothesis is lacking. In this study, we conducted a behavioral experiment using goldfish to investigate how an animal's motion experience influences its interpretation of tilt vs. translation. We examined a reflexive eye movement called the vestibulo-ocular reflex (VOR), which compensatory-rotates the eyes in response to head motion and is known to reflect an animal's three-dimensional head motion estimate. We demonstrated that the VORs of naïve goldfish do not differentiate between translation and tilt at 0.5 Hz. However, following prolonged visual-translation training, which provided appropriate visual stimulation in conjunction with translational head motion, the VORs were capable of distinguishing between the two types of head motion within 3 h. These results were replicated using the Kalman filter model of spatial orientation, which incorporated the variable variance of process noise corresponding to the accumulated motion experience. Based on these experimental and computational findings, we discuss the neural mechanism underlying the resolution of tilt-translation ambiguity within a context analogous to, yet distinct from, previous cross-axis VOR adaptations.

On the optimality of DIA-estimators: theory and applications

In this contribution, we introduce, in analogy to penalized ambiguity resolution, the concept of penalized misclosure space partitioning, with the goal of directing the performance of the DIA-estimator towards its application-dependent tolerable risk objectives. We assign penalty functions to each of the decision regions in misclosure space and use the distribution of the misclosure vector to determine the optimal partitioning by minimizing the mean penalty. As each minimum mean penalty partitioning depends on the given penalty functions, different choices can be made, in dependence of the application. For the DIA-estimator, we introduce a special set of penalty functions that penalize its unwanted outcomes. It is shown how this set allows one to construct the optimal DIA-estimator, being the estimator that within its class has the largest probability of lying inside a user specified tolerance region. Further elaboration shows how these penalty functions are driven by the influential biases of the different hypotheses and how they can be used operationally. Hereby the option is included of extending the misclosure partitioning with an additional undecided region to accommodate situations when it will be hard to discriminate between some of the hypotheses or when identification is unconvincing. By extending the analogy with integer ambiguity resolution to that of integer-equivariant ambiguity resolution, we also introduce the maximum probability estimator within the similar larger class.

Bias-constrained integer least squares estimation: distributional properties and applications in GNSS ambiguity resolution

To accommodate the presence of bounded biases in mixed-integer models, Khodabandeh (2022) extended integer estimation theory by introducing a new admissible integer estimator. The estimator follows the principle of integer least squares estimation and is computed via the integer search method of BEAT. In this contribution, we present the probability distributions of a class of estimators to which the proposed bias-constrained integer least squares estimation belongs. Some important interferometric measuring systems, whose estimation problems can be covered by BEAT, are identified. To show the proposed estimator at work, we apply BEAT to the problem of GLONASS single-differenced (SD) ambiguity resolution. Numerical results of several short-baseline datasets are presented to illustrate why one can achieve more accurate positioning solutions when considering between-receiver SD ambiguity resolution for the cases where carrier phase data are captured on frequency-varying signals with bounded SD receiver phase delays.

Precise positioning in complex environments with GNSS-RTK and weighted linear regression UWB observations

ABSTRACT In complex urban, port, and factory environments, GNSS and UWB encounter significant challenges from signal blockage and multipath effects. This study proposes a GNSS-RTK/WLR-UWB tight integration positioning method, utilizing Weighted Linear Regression to fit missing UWB epochs and introducing dynamic weight adjustment. The track cart and pedestrian experiment demonstrates that the single-epoch positioning accuracy is improved by 53.47%, 65.94%, and 28.03% in E, N and U compared to RTK. The filtering positioning achieves accuracy better than 1 cm in E and N, with a high ambiguity resolution success rate exceeding 99.40%, and reduces time-to-first-fix from 6-13 s to 3 s.

5G assisted GNSS precise point positioning ambiguity resolution

5G assisted GNSS precise point positioning ambiguity resolution