This study focuses on the key challenges in detecting and estimating motion parameters of ground maneuvering targets for airborne radar sensors. The complex unknown motion states of the ground maneuvering target, including velocity, acceleration, and jerk, result in range migrations (RMs) and Doppler frequency migrations (DFMs). These effects severely degrade the long-time coherent accumulation performance of the airborne radar, thereby limiting the reliable detection and precise parameter estimation of maneuvering targets. To address this issue, a new detection and motion parameter estimation method based on the range frequency reversal transform (RFRT) and searching Lv’s distribution (SLVD), i.e., RFRT-SLVD, is proposed. Specifically, the third-order RM (TRM) and quadratic DFM (QDFM) are considered. The proposed method operates as follows: First, RMs are eliminated simultaneously via the RFRT operation, which multiplies the echo by its reversed data in the range frequency and slow-time domains, leveraging the symmetric equal-interval sampling property of the range frequency. Subsequently, a phase compensation function (PCF) related to the jerk is constructed to compensate the QDFM. Finally, the LVD is performed to remove residual DFMs and achieve effective signal energy accumulation. Additionally, the case of a fast-moving target with Doppler ambiguity is analyzed, and a method for estimating three motion parameters is provided. A key advantage of the proposed technique is its ability to directly compensate the RMs without requiring prior knowledge of the maneuvering target, while also avoiding the blind speed sidelobe (BSSL) effect. In comparison with existing algorithms, RFRT-SLVD achieves a balanced trade-off between parameter estimation performance and computational efficiency. Numerical analyses and experiments are conducted to validate the method, assessing its detection capability for ground maneuvering targets, Doppler ambiguity resolution in parameter estimation, computational complexity, and method applicability in multi-target scenarios.

Our visual system usually provides a unique and functional representation of the external world. At times, however, there is more than one compelling interpretation of the same retinal stimulus; in this case, neural populations compete for perceptual dominance to resolve ambiguity. Spatial and temporal context can guide this perceptual experience. Recent evidence shows that ambiguous retinal stimuli are sometimes resolved by enhancing either similarities or differences among multiple ambiguous stimuli. Although rivalry has traditionally been attributed to differences in stimulus strength, color vision introduces nonlinearities that are difficult to reconcile with luminance-based models. Here, it is shown that a tuned, divisive normalization framework can explain how perceptual selection can flexibly yield either similarity-based “grouped” percepts or difference-enhanced percepts during binocular rivalry. Empirical and simulated results show that divisive normalization can account for perceptual representations of either similarity enhancement (so-called grouping) or difference enhancement, offering a unified framework for opposite perceptual outcomes.

Determination, estimation and modeling of satellite-induced code bias for BDS GEO satellites and its impact on wide-lane UPD estimation and wide-lane ambiguity resolution

Abstract Precise point positioning ambiguity resolution (PPP-AR) is a key technique for achieving fast convergence and high-precision positioning in real-time applications. However, the quality of uncalibrated phase delay (UPD) products remains a critical factor influencing ambiguity resolution success, particularly in multi-GNSS environments. This study presents a robust real-time UPD estimation framework that integrates multi-GNSS differential code bias corrections, antenna phase center offset compensation in the Melbourne-Wübbena combination, and a Kalman filter-based strategy with rigorous initialization and quality control. Using 31 days of observations from 170 MGEX stations, the accuracy of CNES real-time orbit and clock products is first assessed, revealing that BDS-3 satellites show poorer clock performance than GPS and Galileo. Relative to CNES/CLS products, the proposed method substantially improves UPD quality. For wide-lane ambiguities, the proportion of residuals within ±0.15 cycle increases from 84.0% to 89.8% for GPS, from 98.7% to 99.2% for Galileo, and from 75.2% to 89.7% for BDS-3. The narrow-lane ambiguities show even greater improvement, with GPS increasing from 75.1% to 86.6%, Galileo from 78.4% to 88.1%, and BDS-3 from 33.6% to 59.8%. In GPS+Galileo+BDS-3 PPP-AR experiments, the proposed method shortens convergence times by 8.3%, 25.0%, and 23.3% in the north, east, and up components, respectively, compared with CNES/CLS. The cumulative distribution of time to first fix also indicates a 6.8% increase in stations achieving ambiguity resolution within 3-18 min. These results demonstrate that the proposed framework effectively enhances real-time UPD quality, thus improving the reliability and efficiency of PPP-AR positioning in global multi-GNSS applications.

To address the slow convergence issue of PPP-B2b, this paper proposes a positioning method that integrates wide-lane ambiguity resolution (WAR) with low earth orbit (LEO) synergistic enhancement. First, based on an uncombined precise point positioning (PPP) model that accounts for the clock constant bias (CCB), the extra-wide lane (EWL) and wide-lane (WL) fractional cycle biases (FCBs) for GPS/BDS-3 were extracted. Subsequently, two hybrid LEO constellations with scales of 96 and 144 satellites were designed, and multi-frequency observations were simulated. Finally, four sets of progressive experiments (float solution, wide-lane fixed solution, LEO-enhanced float solution, and synergistic enhanced solution) were designed to evaluate the enhancement performance. The results demonstrate that both WAR and LEO enhancement can improve the accuracy and convergence performance of PPP-B2b, with temporal complementarity during the convergence stage. Specifically, WAR enhancement improves the initial epoch accuracy to decimeter-level and reduces the average convergence time by over 30%, while LEO enhancement requires multiple epoch accumulations but achieves or even surpasses the WAR enhancement within a short time (e.g., 20-40s) due to its rapid geometric variation advantage, reducing the average convergence time from approximately 20min to 1-2min. The synergistic enhancement of both methods balances the initial epoch parameter estimation accuracy and rapid convergence advantage, achieving improvements in average accuracy and convergence time of over 70% and 90%, respectively.

The performance of the MADOCA-PPP (Multi-GNSS Orbit and Clock Augmentation-Precise Point Positioning) wide-range ionospheric correction requires further investigation during periods of high ionospheric activity, particularly regarding its impact on the convergence time and positioning accuracy of both PPP and PPP with Ambiguity Resolution (PPP-AR). Thus, the present study selects the month with the highest average Kp index between January 2023 and May 2025 and conducts positioning analyses at nine stations. Results indicate that applying wide-range ionospheric corrections reduces PPP convergence time by 47% in static mode and 54% in kinematic mode. When these corrections are integrated into PPP-AR, they shorten the convergence time by 69% in static mode and 72% in kinematic mode. Moreover, PPP-AR enhanced with wide-range ionospheric corrections achieves the highest positioning accuracy across both modes: in static mode, the horizontal and vertical root mean square errors (RMSEs) are approximately 5.2 cm and 6.9 cm, respectively, while in kinematic mode, these values are 5.6 cm and 8.0 cm. These findings demonstrate that the wide-range ionospheric corrections provided by the MADOCA-PPP service effectively enhance PPP performance during periods of heightened ionospheric activity.

Regressive eye movements are commonly observed in response to syntactic ambiguities, yet their characteristics and role in ambiguity resolution remain unclear. This study examined two key questions about rereading in Chinese garden-path sentences: (1) whether rereading is selective, meaning that readers allocate more or longer fixations to specific regions following garden-path disambiguation and (2) whether rereading facilitates comprehension by aiding the recovery of the globally correct interpretation. We therefore conducted an eye-tracking experiment on sentences featuring a naturally occurring syntactic ambiguity (NP1 + VP + NP2 + de + NP3). The results provide clear evidence of selective rereading, as readers refixated the ambiguous region after encountering processing difficulty. However, these refixations did not aid ambiguity resolution, suggesting that while regressions reflect sensitivity to processing disruptions, they do not always contribute to successful reinterpretation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Abstract High-density urban environments severely impair smartphone Global Navigation Satellite System (GNSS) positioning due to Non-Line-of-Sight (NLOS) signals and limited satellite visibility, leading to reduced accuracy and continuity. Three-Dimensional Map-aided (3DMA) GNSS methods partially solve the problems but still much rely on noisy pseudorange measurements, while the resolution of carrier-phase ambiguities remain challenging, limiting their robustness in complex urban areas. To overcome these challenges, this study introduces a novel Factor Graph Optimization (FGO) framework that tightly integrates 3D map constraints with multiple GNSS observations. First, a Shadow Matching (SDM) scoring strategy is proposed by incorporating Time-Differenced Carrier Phase (TDCP) constraints. Second, a map-matching probability approach is applied to identify a unique candidate road segment, thereby reducing solution ambiguity. Third, a Random Sample Consensus (RANSAC)-based region growing clustering algorithm is designed to manage multimodal high-score points and ensure unique clustering. Finally, a factor graph model is constructed that fuses pseudorange, Doppler, and TDCP observations with 3D map constraints, significantly enhancing positioning accuracy and stability. Field experiments in typical urban scenarios show that the proposed method outperforms existing SDM techniques such as road constraint and region-growing clustering, as well as advanced GNSS optimization frameworks, in terms of both positioning accuracy and trajectory continuity. Specifically, the proportion of horizontal positioning errors within 3 m and 5 m reached 76.7% and 93.1%, respectively, substantially exceeding those achieved by the advanced GNSS multi-source fusion framework (63.4% and 79.3%).

Improving PPP ambiguity resolution with a modified particle swarm optimization method

Rethinking ambiguity across species.

Integrated orbit determination and gravity field recovery enhanced by integer ambiguity resolution for low earth orbit satellites

Abstract Traditionally, global navigation satellite system (GNSS) observations in precise point positioning were processed using geometric ionosphere-free (GIF) code and phase linear combinations (LC). With multi-frequency observations of modernized GNSS, the processing of undifferenced and uncombined (UDUC) observations has gained popularity, often attributed to its increased flexibility and generality. Building on the theoretical foundation of UDUC processing, this work derives an equivalent LC-based network processing approach that maintains the full generality of the UDUC approach while offering some practical advantages. The approach makes use of the following types of LCs: (1) geometry-free and ionosphere free (GFIF), (2) geometric ionosphere-free (GIF), and (3) ionospheric geometry-free (IGF). When processing the GFIF LCs, biases and ambiguities can be estimated. The GFIF model enables compact modeling by reducing continuous observation arcs to a single observation and supports (extra-) wide-lane ambiguity resolution. To obtain the ionosphere-free model, i.e., an equivalent reformulation of the UDUC approach where epoch-wise and line of sight specific ionospheric delays are assumed, exactly one GIF LC per line-of-sight and epoch is added to the GFIF LCs. To obtain the geometry-free model, commonly used for ionospheric modeling, exactly one IGF LC is added to the GFIF LCs per line-of-sight and epoch. Adding both the GIF and IGF LCs to the GFIF LCs yields an exact reformulation of the UDUC with no implicit assumptions regarding ionospheric or geometric parameters. Finally, a brief runtime analysis of LC-based models shows case-dependent efficiency gains over UDUC implementations.

Machine Reading Comprehension (MRC) refers to generating precise responses to the users’ queries from text content using natural language processing. The exponential growth and complexities of online content have made it difficult to surf the required information by navigating through several web pages to retrieve precise and accurate answers to the users’ questions. Therefore, MRC has emerged as an active and growing research area in recent years. The existing studies highlight the significance of deep learning models yet lack in resolving ambiguity, especially in complex passages. Bidirectional encoder representations from transformers have addressed passage ambiguity resolution, but their complexity results in the demand for high computational resources and a large volume of data for better text comprehension. To address passage ambiguities and reduce computational costs, this study fine-tunes the DistilBERT model for the MRC task. The resulting model termed Distil-BERT-MRC uses a reduced architecture ensuring efficiency while maintaining competitive performance. The results of the detailed analysis demonstrate that Distil-BERT-MRC attained up to 90.23% exact match and 91.42% F1 score on the WikiQA dataset. Moreover, to assess the generalizability and resource utilization, extensive experiments were performed on SQuAD 2.0, NewsQA, and Natural Questions using recent transformer models, including RoBERTa and XLNet. Overall, our findings confirm that distilled transformer models provide a resource-efficient and effective approach for MRC tasks.

The development of a word sense disambiguation component of a machine translation (MT) system is faced with many challenges. One of these is the incidence of contextual tonal variation in the Yorùbá language which makes the use of statistical based approach highly expensive for resolving lexical ambiguity in the language. This study examined the procedures underlining the resolution of lexical ambiguity in the context of Yorùbá-to-English MT system and developed a knowledge based approach which makes use of path-based similarity measurement between two instances of an ambiguous word to determine its right sense. This model achieved an accuracy of 96.1% for transitive verbs, 90.2% for intransitive verbs, with an overall accuracy of 94.6%, which is comparable with the high-performing supervised WSD, and a coverage of 69.3%.This study suggests a method that can be used to address ambiguity resolution in other low resource languages.

Abstract Precise point positioning (PPP) is a widely adopted absolute GNSS positioning technique known for its high accuracy. However, it suffers from long convergence times, which is its main limitation. To address this, PPP with ambiguity resolution (PPP-AR) methods have emerged as a promising approach to enhance both convergence and positioning accuracy. In recent years, four International GNSS Service (IGS) analysis centers—Center for Orbit Determination in Europe (CODE), Centre National d’Etudes Spatiales, German Research Centre for Geosciences, and Wuhan University Multi-GNSS Experiment—have begun to provide observable-specific signal bias (OSB) products, which have gained considerable attention for the facilitation of PPP-AR implementation. This study evaluates the recent performance of these products throughout the year 2024 using the open-source multi-GNSS PPP software, PPPH, which we adapted for PPP-AR solutions. First, data availability was assessed by computing the annual availability percentage for each product and the average number of available OSB values for dual-frequency observations; short- and long-term OSB stability was also analyzed. Performance evaluation was then conducted using 24 h datasets from 20 global IGS stations. According to the results, PPP-AR is more favorable for GPS and Galileo than for BeiDou in single-constellation configurations. Moreover, the results indicate that OSB product selection can significantly influence convergence behavior, depending on the constellation used, while the contribution of PPP-AR on positioning accuracy after the convergence period is limited, regardless of the choice of OSB product. Notably, CODE stands out among the analysis centers with its superior performance and data availability. When used in PPP-AR, CODE’s OSB products yield the shortest average convergence times—12.5 min for GPS-only, 14.4 min for Galileo-only, and 7.8 min for GPS + Galileo—to achieve decimeter-level 3D positioning accuracy.

Abstract Since the GPS satellite flex power mode was modified on day 045 of 2020, it has had a significant impact on the kinematic orbit accuracy and ambiguity resolution performance of the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellites. This study investigates the mechanisms behind this impact. By analyzing pseudo-range multipath and signal-to-noise ratio (SNR) data, it was found that during flex power activation, the P2 observations exhibited notably larger multipath errors and frequent abrupt fluctuations, while SNR also showed fluctuations at certain epochs, indicating a marked decline in the quality of P2 observations during this period. To address this, this study proposes strategies for SNR-jump-assisted cycle slip detection and pseudo-range prior precision adjustment to improve the kinematic precise orbit determination during flex power activation. Experimental results demonstrate that the proposed methods significantly improve the kinematic orbit accuracy of both float and fixed solutions for the GRACE-FO satellites, achieving a 10% improvement in three-dimension orbit accuracy.

Robo-advisors have integrated into financial advisory services, providing consumers with ‎regular investment guidance. Yet, it remains unclear how their visual design affects decision-making in high-risk and uncertain situations, like taking investment advice. This study focuses ‎on preferences and willingness to adopt Robo-Advisors in stock market investments. And, ‎investigated whether the Robo-Advisors are suitable for small investors or investors with less ‎experience in the Stock market. This study investigates the phenomenon of innovation aversion in ‎financial advising, focusing on how stock market investors respond to ambiguity and ‎uncertainty associated with emerging advisory technologies. Through a mixed-method ‎approach combining surveys and in-depth interviews with individual investors, the study ‎reveals that while technological innovation offers potential benefits in terms of efficiency and ‎cost-effectiveness, perceived complexity and lack of personal interaction contribute ‎significantly to innovation aversion. Inferential statistics like one-way ANOVA, Chi-Square ‎, and Regression were used to analyse the adoption and satisfaction of using Robo-Advisors ‎from the data of 119 respondents among residing and Non-residing Indians with the help of ‎IBM SPSS. Thematic analysis was used on qualitative data. The study reveals that the Non-resident Indians have greater satisfaction with these Robo-advisors’ platforms. Beginner ‎investors prefer Robo-advisors for their straightforwardness, while experienced investors tend ‎to be more cautious. Both groups, however, exhibit limited awareness of Robo-advisors, ‎despite the potential benefits they present‎.

HighlightsWhat are the main findings?We present a novel GNSS-based attitude tracking method for short baselines that significantly reduces computational complexity without compromising the accuracy achieved by established algorithms.We introduce an a priori error model for GNSS measurement errors that lends itself to a clear and intuitive geometric interpretation.What is the implication of the main finding?By improving computational efficiency in integer ambiguity resolution, the proposed method simplifies the implementation of real-time attitude tracking algorithms, especially in systems that combine GNSS with data from other sensors.The paper addresses the problem of attitude determination using Global Navigation Satellite System (GNSS) measurements from multiple antennas mounted on a navigation platform. To achieve attitude determination by GNSS with typical accuracy down to tenths of a degree for one-meter baselines, GNSS phase measurements are employed. A key challenge with phase measurements is the presence of unknown integer ambiguities. Consequently, the attitude determination problem traditionally reduces to a nonlinear, non-convex optimization problem with integer constraints. No closed-form solution to this problem is known, and its real-time calculation is computationally intensive. Given an a priori initial attitude approximation, we propose a new algorithm for attitude tracking based on the reduction of the nonlinear orthogonality-constrained attitude estimation problem to a linear integer least squares problem, for which numerical methods are well known and computationally much less demanding. Additionally, a simple a priori model for GNSS measurement error variance is introduced, grounded on the geometry of satellite signal propagation through vacuum and the Earth’s atmosphere, providing a clear physical interpretation. Applying the algorithm to a real dataset collected from a quasi-static multi-antenna, multi-GNSS system with sub-meter baselines, we obtain promising results.

GPS/Galileo precise time transfer with ambiguity resolution using products from multiple analysis centres

An Enhanced Stereo UWB Bearing Scheme via Network Ambiguity Resolution and Online Phase Calibration