Ambiguity Resolution Research Articles

Performance of kinematic GPS PPP AR under different ionospheric scintillation conditions

To analyze the performance of Global Positioning System (GPS) Precise Point Positioning (PPP) Ambiguity Resolution (AR) under various ionospheric scintillation environments, we selected GPS data of ionospheric scintillation monitoring receivers (ISMR) and geodetic receivers from high-latitude and low-latitude regions during 2021–2023. We conducted GPS Kinematic PPP using Canadian Spatial Reference System (CSRS) PPP and used the maximum and average values of σφ and S4 as indicators to characterize the intensity of phase scintillation and amplitude scintillation for ISMRs, while the maximum and average values of Rate of Total Electron Content Index (ROTI) to characterize the intensity of ionospheric scintillation for geodetic receivers. We also used ambiguity resolved percentage (ARP) and the 3-Dimensional Root Mean Square (3D RMS) of positioning errors as performance metrics for PPP-AR and PPP. As the maximum and average values of ionospheric scintillation indices increase, the PPP-ARP for high-latitude ISMRs decreases to 90.0 %, and the 3D RMS can reach 0.204 m. During ionospheric quiet conditions, positioning accuracy of GPS PPP can reach the cm-mm level. As the geomagnetic latitude decreases, the scintillation effects on PPP-AR and PPP become more pronounced for high-latitude ISMRs. Compared with high-latitudes, ISMRs located at low-latitudes are more severely affected by scintillation, with PPP-ARP at the station HNLW in China and the station UFBA in Brazil dropping to 80.0 % and 60.0 %, respectively. The 3D RMS can reach 0.288 m and 0.341 m. The results of geodetic receives are consistent with those of ISMRs, with 3D RMS of positioning errors up to 0.240 m and 0.256 m for CYNE and MAL2, respectively, while PPP-ARP drops to 60.4 % and 79.6 %. Additionally, we observed a strong positive correlation between ionospheric scintillation duration and the maximum values of σφ, S4 and ROTI.

A flexible method for BDS-3 multi-frequency phase observable-specific biases estimation and its PPP analysis with different ambiguity resolution strategies

With the rapid progress of Global Navigation Satellite Systems (GNSS), integer ambiguity resolution has gradually become one of the key factors that affecting Precise Point Positioning (PPP) to achieve high-precision positioning results and fast convergence. Fortunately, the proposal of observable-specific code/phase biases (OSB) has greatly alleviated this problem tremendously. Different from obtaining OSB by dual-frequency linear combinations, this research proposed BeiDou Navigation Satellite System (BDS) OSB estimation method from raw observations. This method allows for more flexible estimation in a global network, without the need to compose the complex linear combinations when involving multi-frequency measurements processing. The BDS-3 observation data collected from 112 stations over one month (October 2022), containing B1C/B1I/B2a/B2b/B3I frequencies, were used for five-frequency phase OSBs estimation. Subsequently, static and kinematic PPP were executed utilizing two different ambiguity resolution strategies to corroborate the efficacy of this method. The results revealed that the BDS-3 five-frequency phase OSBs possessed good monthly stability, with standard deviation less than 0.3 cycle. High position accuracy was achieved in the static mode by fixing the ambiguity directly, with root mean square (RMS) of 3.0, 4.8 and 9.9 mm. Meanwhile, the ambiguity fixed rate was maintained at a high level around 96 % in multi-frequency tests. Simultaneously, the kinematic mode also presented the centimeter-level dynamic positioning results and the positioning accuracy improved by 55, 31 and 17 % in the East, North and Up components, respectively. Compared to the float solutions, the adoption of multi-frequency OSBs significantly accelerated the convergence speed by 55 % on average.

Ranking-based neural network for ambiguity resolution in ACTS

The reconstruction of particle trajectories is a key challenge of particle physics experiments, as it directly impacts particle identification and physics performances while also representing one of the main CPU consumers of many high-energy physics experiments. As the luminosity of particle colliders increases, this reconstruction will become more challenging and resourceintensive. New algorithms are thus needed to address these challenges efficiently. One potential step of track reconstruction is ambiguity resolution. In this step, performed at the end of the tracking chain, we select which tracks candidates should be kept and which must be discarded. The speed of this algorithm is directly driven by the number of track candidates, which can be reduced at the cost of some physics performance. Since this problem is fundamentally an issue of comparison and classification, we propose to use a machine learning-based approach to the Ambiguity Resolution. Using a shared-hits-based clustering algorithm, we can efficiently determine which candidates belong to the same truth particle. Afterwards, we can apply a Neural Network (NN) to compare those tracks and decide which ones are duplicates and which ones should be kept. This approach is implemented within A Common Tracking Software (ACTS) framework and tested on the Open Data Detector (ODD), a realistic virtual detector similar to a future ATLAS one. This new approach was shown to be 15 times faster than the default ACTS algorithm while removing 32 times more duplicates down to less than one duplicated track per event.

Naïve Bayes Approach for Word Sense Disambiguation System With a Focus on Parts-of-Speech Ambiguity Resolution

Naïve Bayes Approach for Word Sense Disambiguation System With a Focus on Parts-of-Speech Ambiguity Resolution

Resolution of Left-Right Ambiguity for Underwater Bearing Only Tracking

Resolution of Left-Right Ambiguity for Underwater Bearing Only Tracking

Enhancing Smartphone Relative Positioning With Partial Wide-Lane Ambiguity Resolution: Path to Real-time, Decimeter-Level Positioning in User Environments

Enhancing Smartphone Relative Positioning With Partial Wide-Lane Ambiguity Resolution: Path to Real-time, Decimeter-Level Positioning in User Environments

AdaptiveClick: Click-Aware Transformer With Adaptive Focal Loss for Interactive Image Segmentation.

Interactive image segmentation (IIS) has emerged as a promising technique for decreasing annotation time. Substantial progress has been made in pre-and post-processing for IIS, but the critical issue of interaction ambiguity, notably hindering segmentation quality, has been under-researched. To address this, we introduce AdaptiveClick - a click-aware transformer incorporating an adaptive focal loss (AFL) that tackles annotation inconsistencies with tools for mask-and pixel-level ambiguity resolution. To the best of our knowledge, AdaptiveClick is the first transformer-based, mask-adaptive segmentation framework for IIS. The key ingredient of our method is the click-aware mask-adaptive transformer decoder (CAMD), which enhances the interaction between click and image features. Additionally, AdaptiveClick enables pixel-adaptive differentiation of hard and easy samples in the decision space, independent of their varying distributions. This is primarily achieved by optimizing a generalized AFL with a theoretical guarantee, where two adaptive coefficients control the ratio of gradient values for hard and easy pixels. Our analysis reveals that the commonly used Focal and BCE losses can be considered special cases of the proposed AFL. With a plain ViT backbone, extensive experimental results on nine datasets demonstrate the superiority of AdaptiveClick compared to state-of-the-art methods. The source code is publicly available at https://github.com/lab206/AdaptiveClick.

Rapid and Reliable Integer Ambiguity Resolution in BDS-3 and Galileo Penta-Frequency Observations over Long Baselines

Rapid and Reliable Integer Ambiguity Resolution in BDS-3 and Galileo Penta-Frequency Observations over Long Baselines

A Dual-Frequency Phase Ambiguity Resolution Method for FMCW Radar High-Accuracy Ranging

A Dual-Frequency Phase Ambiguity Resolution Method for FMCW Radar High-Accuracy Ranging

Application of Variational Bayesian Filtering Based on T-Distribution in BDS Dynamic Ambiguity Resolution

Application of Variational Bayesian Filtering Based on T-Distribution in BDS Dynamic Ambiguity Resolution

GPS/BDS Dual-Antenna Attitude Determination With Baseline-Length Constrained Ambiguity Resolution: Method and Performance Evaluation

GPS/BDS Dual-Antenna Attitude Determination With Baseline-Length Constrained Ambiguity Resolution: Method and Performance Evaluation

Resilient Ambiguity Resolution Strategy in GNSS Real-Time Kinematic Positioning for Urban Environments

Resilient Ambiguity Resolution Strategy in GNSS Real-Time Kinematic Positioning for Urban Environments

4D High-Resolution Imagery of Point Clouds for Automotive mmWave Radar

In the community of automotive millimeter wave radar, the recently developed concept of four-dimensional (4D) radar can provide high-resolution point clouds image with enhanced imaging performance. Currently, the density of point clouds for single-frame image is usually too sparse to satisfy the demands of target classification and recognition due to the limitation of Doppler and angle resolutions. To address the aforementioned issues, a novel algorithm is proposed for 4D high-resolution imagery generation of point clouds with extremely high Doppler and angle resolutions in this paper. For high Doppler resolution with high-dynamic, a novel velocity ambiguity resolution algorithm is proposed using a dual pulse repetition frequency (dual-PRF) waveform design embedded in an innovative time-division multiplexing & Doppler-division multiplexing MIMO (TDM-DDM-MIMO) framework. Meanwhile, an attractive complex-valued deep convolutional network (CV-DCN) of super-resolution direction-of-arrival (DOA) estimation is proposed only using single-frame data. To be specific, a spatial smoothing operator on array data is applied as input of the network, and a CV-DCN is designed to learn the transformation of the spatial spectrum from the end-to-end to effectively protect the spectrum extraction. Furthermore, experimental analysis is performed to confirm the effectiveness of the proposed super-resolution DOA estimation algorithm. Finally, the 4D high-resolution imagery of point clouds is obtained by experiments in the parking lot.

A Velocity Ambiguity Resolution Algorithm Based on Improved Hypothetical Phase Compensation for TDM-MIMO Radar Traffic Target Imaging

A Velocity Ambiguity Resolution Algorithm Based on Improved Hypothetical Phase Compensation for TDM-MIMO Radar Traffic Target Imaging

Relative Clause Processing and Attachment Resolution across Languages: Tatar–Russian–English Trilinguals

The study investigates psycholinguistic mechanisms of sentence parsing and ambiguity resolution by balanced Tatar–Russian bilinguals who learnt English as their additional language. We check the parser’s sensitivity to the selectional properties of the matrix verb and/or social conventions in processing and attachment resolution of ambiguous relative clauses (RCs). We chose English and Russian because they have a documented preference for low attachment (LA) and high attachment (HA), respectively, and Tatar, as we have found out in earlier work, has no attachment ambiguity. We conducted a self-paced reading task in English and Russian which returned 61% HA in Russian, 49% HA in English. It was followed by a pen-and-paper translation task. The translation post-test checked whether an attachment preference demonstrated in either English or Russian showed in RC translations into Tatar. The results return an 80% preference for LA in English–Tatar translations and 61% in Russian–Tatar translations. Both syntactic information and world knowledge influence online RC processing in Russian and English. Therefore, the multilingual parser incorporates information from multiple sources in either L1 or Ln processing. The parser may favor LA as a default parsing option while maintaining sensitivity to individual grammars (Russian), where this preference should be overridden.

A self-paced reading study of context effects in the processing of aspectual verbs in Mandarin

Abstract Research in the past few years has investigated the semantic complexity of expressions with aspectual verbs followed by entity-denoting complements such as finish the book that led to processing costs cross-linguistically. The Structured Individual Hypothesis (SIH) proposes that aspectual verbs lexically encode a function whose value (dimension) must be resolved. This ambiguity resolution is hypothesized to occur at the verb’s complement, where a specific dimension is selected based on context (Piñango & Deo, 2016). In light of the critical role of the context in SIH, recent research (Lai et al., 2023; Lai & Piñango, 2019) has investigated how the interpretations of sentences with aspectual verbs were affected by biased contexts in an offline sentence acceptability judgment study and an online eye-tracking study. However, results of the two studies showed that biased contexts disambiguated the interpretations of aspectual verb expressions offline while processing costs in biased contexts were not found to attenuate costs in real time. The reason why conflicting results were found offline versus online and the timecourse of context effects remain unclear, but in our view it may be due to pragmatic contexts, i.e., descriptions of the utterance context used to infer the salient reading of the utterance. We used grammatical contexts – two classes of adverbs – in a self-paced reading study to examine context effects for sentences with aspectual verbs in Mandarin. We found that biased grammatical contexts not only affected the interpretations in the offline task, but crucially facilitated processing in the online experiment as well. We conclude that biased grammatical contexts predetermine the interpretations of aspectual verb expressions immediately in real time.

BDS-3/Galileo multi-frequency phase-only model for long baseline with best integer equivariant estimation

Currently, long baselines generally adopt phase-code models that include both phase and pseudorange observations, such as dual-frequency ionosphere-free (PC-DFIF) and multi-frequency uncombined (PC-MFUC) models. The more significant unmodeled errors in pseudorange are likely to reduce ambiguity resolution (AR) performance, and the phase-only model with only phase observations involved is expected to avoid this problem. In view of the fact that existing phase-only research is mainly focuses on GPS/BDS-2 triple-frequency signals, a BDS-3/Galileo multi-frequency phase-only (MFPO) model is proposed in this paper. Four linearly independent extra-wide-lane (EWL) phase ranges (i.e. IR1, IR2, EWL3 and EWL4) are determined stepwise with a sliding window, among which the first two belong to ionosphere-reduced (IR) combination. In the determination of EWL3/EWL4, in addition to theoretical precision, the amplified phase multipath is also considered. The aforementioned four EWL phase ranges are then equivalently transformed to wide-lane (WL) combinations with smaller coefficients, and further incorporated with narrow-lane (NL) equation to construct MFPO model. Meanwhile, the best integer equivariant (BIE) is introduced to improve AR performance. The MFPO model is evaluated with real-tracked long baseline data. The performance of EWL phase ranges can be significantly improved after phase multipath is considered. The float solution, fixed solution and BIE solution of MFPO model all achieve centimeter-level accuracy. However, during convergence stage, incorrect fixing might exist in fixed solution, while BIE solution can improve this situation. The MFPO model is further compared with commonly used PC-DFIF and PC-MFUC models. The accuracy of the three models after convergence is basically the same, while the MFPO model has higher accuracy during convergence stage.

FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSS

Abstract. With the growing interest in autonomous driving, accurate vehicle positioning remains an open problem, especially in urban environments. According to regulatory organisations, the vehicle positioning accuracy is required to be centimetre-level. As the most used positioning technique which provides globally referenced positioning solutions, GNSS is the fundamental component for realising real-time vehicle positioning, usually through the RTK approach. However, RTK requires a nearby reference station to enable integer ambiguity resolution for the ultra-precise carrier phase observations. In comparison, PPP makes use of State-Space Representation (SSR) corrections produced by global networks for satellite orbits and clocks to facilitate phase-based positioning. Moreover, IGS now offers Real-Time Service (RTS) to transmit such corrections. Notably, the major drawback of PPP is that it takes a long time to converge to precise solutions due to the carrier phase ambiguities being real-valued, which can be severely elongated when real-time corrections and low-cost GNSS receivers are used. In this paper, a tightly coupled positioning method is proposed, which shortens RT-PPP convergence to seconds by using lidar measurements referenced from an HD map through deep learning. The lidar measurements are generated by point cloud registration and weighted by their intensity values and geometric distributions, and are then combined with RT-PPP in an Extended Kalman-Filter (EKF), thus achieving fast convergence. Experimental results show that the proposed method achieves and maintains centimetre-level accuracy within 2 seconds using a low-cost UBLOX F9P receiver, which is a significant improvement as compared to the decimetre-level accuracy obtained from standalone RT-PPP.

Correction to “Joint Velocity Ambiguity Resolution and Ego-Motion Estimation Method for mmWave Radar” [Aug 23 4753-4760

Correction to “Joint Velocity Ambiguity Resolution and Ego-Motion Estimation Method for mmWave Radar” [Aug 23 4753-4760

Distortion‐less carrier phase tracking space‐time adaptive processor based on power inversion criterion for GNSS anti‐jamming receiver

AbstractSpace‐time adaptive processor (STAP) has been widely used for global navigation satellite system (GNSS) anti‐jamming receiver due to its good anti‐jamming performance. When direction of satellite is unknown, STAP can be implemented based on power inversion (PI) criterion. However, existing space‐time PI algorithm will introduce tens to hundreds of degrees biases into carrier phase, and sometimes will even cause cycle slips, which will reduce the success rate of ambiguity resolution, ultimately deteriorating positioning accuracy. A distortion‐less carrier phase tracking space‐time PI algorithm is proposed. The main novelty is that the proposed algorithm keeps the coefficients of the temporal taps as real values by imposing constraints on the weights of the antenna array. Several experiments are implemented to verify the effectiveness of the proposed algorithm. For comparison, the results of PI algorithm and minimum variance distortion‐less response (MVDR) algorithm are shown. Results show that when the number, style, and direction of interferences and the direction of GNSS signal vary, different degrees of biases are introduced into carrier phases for the PI and the MVDR algorithm. However, no bias is introduced into the proposed algorithm. As a result, the effectiveness of the proposed algorithm is verified.