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In this contribution we introduce the dual mixed-integer least-squares problem and study it in relation to its primal counterpart. The dual differs from the primal formulation in the order in which the integer ambiguity vector a ∈ Z n and baseline vector b ∈ R p are estimated. As not the ambiguities, but rather the entries of b are usually the parameters of interest, the attractiveness of the dual formulation stems from its direct computation of b. It is shown that this potential advantage relies on the ease with which an implicit integer least-squares problem of the dual can be solved. For the convoluted cases, we introduce two methods of simplifying approximations. To be able to describe their quality, we provide a complete distributional analysis of their estimators, thus allowing users to judge whether or not the approximations are acceptable for their application. It is shown that this approach implicitly introduces a new class of admissible integer estimators of which we also determine the pull-in regions. As the dual function is shown to lack convexity, special care is required to be able to compute its global minimizer b ˇ . Our proposed method, which has finite termination with a guaranteed ϵ -tolerance, is constructed from combining the branch-and-bound principle, with a special convex-relaxation of the dual, to which the projected-gradient-descent method is applied to obtain the required bounds. Each of the method's three constituents are described, whereby special emphasis is given to the construction of the required continuously differentiable, convex lower bounding function of the dual. [ABSTRACT FROM AUTHOR]

The looking-down mode of space airship bistatic radars faces complex sea–land clutter, and the mode of wide-range surveillance and the over-sight detection of the satellite platform generates a low SNR and range–Doppler ambiguity. The method traditionally used involves the transmission of multiple Pulse Repetition Frequencies (PRFs) and correlating them to solve the ambiguity. However, with a low SNR, the traditional disambiguation fails due to the large number of false alarms and target omissions. In order to solve this problem, a new algorithm for multi-target joint detection and range–Doppler disambiguation based on an ambiguity matrix is presented. Firstly, all possible state values corresponding to the ambiguous sequence are filled into the ambiguity matrix one by one. Secondly, the state values in the matrix cell are divided into several groups of subsequences according to the PRF. By disambiguating multiple sets of subsequences, performing subsequence fusion, and then undertaking point aggregation, the targets can be effectively detected in scenarios with a strong clutter rate, the false alarms can be suppressed, and the disambiguation of the range and Doppler is completed. The simulation shows that the proposed algorithm has the strong ability to detect targets and perform ambiguity resolution in the scenario of a multi-target and multi-false alarm. [ABSTRACT FROM AUTHOR]

The Neutrosophic Numeral System Algorithms are a set of techniques designed to handle uncertainty and ambiguity in numerical data. These algorithms use Neutrosophic Set Theory, a mathematical framework that deals with incomplete, indeterminate, and inconsistent information. In this paper, we provide an overview of different approaches used in Neutrosophic Numeral System Algorithms, including Neutrosophic Binary System, Neutrosophic Decimal System, and Neutrosophic Octal System. These systems use different bases and representations to account for degrees of truth, indeterminacy, and falsity in numerical data. We also explore the relationship between Neutrosophic Numeral System Algorithms and Number Neutrosophic Systems, which are another type of Neutrosophic System used for representing numerical data. Number Neutrosophic Systems use Neutrosophic Numbers to represent degrees of truth, indeterminacy, and falsity in numerical data, and they can be used in conjunction with Neutrosophic Numeral System Algorithms to handle uncertainty and ambiguity in decision-making and artificial intelligence applications. Moreover. We discuss the advantages and disadvantages of each algorithm and their potential applications in various fields. Finally, we highlight the importance of Neutrosophic cryptography in addressing uncertainty and ambiguity in decision making and artificial intelligence and discuss future research directions. Understanding Neutrosophic Numeral System Algorithms and their relationship with Number Neutrosophic Systems is crucial for developing effective techniques for handling uncertainty and ambiguity in numerical data in decision-making, pattern recognition, and artificial intelligence applications. [ABSTRACT FROM AUTHOR]

What happens when an algorithm is added to the work of an expert group? This study explores how algorithms pose a practical problem for experts. We study the introduction of a Probabilistic DNA Profiling (PDP) software into a forensics lab through interviews and court admissibility hearings. While meant to support experts' decision-making, in practice it has destabilized their authority. They respond to this destabilization by producing alternating and often conflicting accounts of the agency and significance of the software. The algorithm gets constructed alternately either as merely a tool or as indispensable statistical backing; the analysts' authority as either independent of the algorithm or reliant upon it to resolve conflict and create a final decision; and forensic expertise as resting either with the analysts or with the software. These tensions reflect the forensic 'culture of anticipation', specifically the experts' anticipation of ongoing litigation that destabilizes their control over the deployment and interpretation of expertise in the courtroom. The software highlights tensions between the analysts' supposed impartiality and their role in the courtroom, exposing legal and narrative implications of the changing nature of expertise and technology in the criminal legal system. [ABSTRACT FROM AUTHOR]

Grid theory is rather commonly-used through out the research of integer ambiguity. In order to promote the efficiency of computation, it is of great necessity to reduce the correlations of the grid basis through the reduction. The classical reduction algorithm is known as the LLL (Lenstra–Lenstra–Lovász) algorithm. So as to further enhance the reduction effect, the deep-insertion LLL algorithm can be utilized as an alternative to the basis vector exchange algorithm. In practice, the deep-insertion LLL algorithm can achieve a better reduction effect, but it requires more time for reduction. The PotLLL algorithm replaces the basis vector exchange condition of deep-insertion LLL with an improving in the basis quality, and it can run in polynomial time, but with certain limitations. Therefore, this article proposes a global deep-insertion PLLL algorithm (GS-PLLL) to address the issue of integer ambiguity. GS-PLLL adopts a global strategy for deep-insertion processing, and introduces a rotation sorting method for preconditioning the grid basis. Comparative evaluations were conducted using simulation experiments and real-world measurements on the LLL, DeepLLL, PotLLL, and GS-PLLL algorithms. The experimental results indicate that the GS-PLLL algorithm achieves a better reduction effect than the PotLLL algorithm while improving the efficiency of reduction. [ABSTRACT FROM AUTHOR]

Real-Time (RT) Precise Point Positioning (PPP) uses precise satellite orbits and clock corrections, and employs a separate receiver for positioning. With the growing demand, RT PPP is becoming an increasingly popular research topic. The ambiguity resolution (AR) can significantly improve the positioning accuracy and convergence time of PPP, so it is essential to study PPPAR in RT mode. In this paper, 37 MGEX stations from around the world are chosen, and the RT orbit, clock, and phase biases products broadcast by the Centre National d'Etudes Spatiales (CNES) are applied to PPPAR. Additionally, the residuals of the RT phase biases products, convergence time, and positioning accuracy are investigated. The results indicate that GPS products have the best quality of AR, with wide-lane (WL) and narrow-lane (NL) residuals of 98.9% and 95.3%, respectively, within ±0.25 cycles. Within ±0.25 cycles, the WL and NL residuals of the Galileo are 98.2% and 94.3%, respectively. Within ±0.25 cycles, the (Beidou Navigation Satellite System) BDS has a poor quality of AR, with WL and NL residuals of 97.3% and 73.1%, respectively. Due to the poor quality of the BDS AR, the convergence time of the BDS is not calculated in this paper. The convergence time of other systems is significantly reduced after AR processing, and the convergence time of the GPS/Galileo combination is the fastest, being 17.14 min in kinematic mode and only 11.85 min in static mode. The positioning accuracy of the GPS, Galileo, GPS/Galileo, and GPS/Galileo/BDS in the E and U directions is significantly improved after PPPAR. [ABSTRACT FROM AUTHOR]

The fast and accurate solution of integer ambiguity is the key to achieve GNSS high-precision positioning. Based on the lattice theory of high-dimensional ambiguity solving, the reduction time consumption is much larger than the search time consumption, and it is especially important to improve the efficiency of the lattice basis reduction algorithm. The Householder QR decomposition with minimal column pivoting is utilized to pre-sort the basis vectors and reduce the number of basis vector exchanges during the reduction process by partial size reduction and relaxing the basis vector exchange condition to improve the reduction efficiency of the LLL algorithm. The improved algorithm is validated using simulated and measured data, respectively, and the performance advantages and disadvantages of the improved algorithm are evaluated from the perspectives of the extent of reduction basis orthogonality and the quality of reduction basis size reduction. The results show that the improved LLL algorithm can significantly reduce the number of basis vector exchanges and the reduction time consumption. The HSLLL and PSLLL algorithms with the Siegel condition as the basis vector exchange condition have a better reduction effect, but are slightly less stable. The PLLLR algorithm significantly improves the search ambiguity resolution efficiency, which is conducive to the rapid realization of ambiguity resolution. [ABSTRACT FROM AUTHOR]

Most of the deep-learning based dehazing models have fixed receptive filed after the parameter are fixed. As a result, the dehazing network cannot adopt the optimal mode for dehazing each specific scene, resulting in ambiguity and distortion in the results. In view of these problems, this study proposes a dynamic receptive field feature selection dehazing network. A feature-attention atrous block with atrous convolution is designed as the basic module of the network. Multiple feature attention atrous blocks with different atrous rates are used in parallel to extract multi-scale features. Dynamic feature fusion is performed on these features to form a dynamic receptive field block. Multiple dynamic receptive field blocks are combined with residual connections to form a deep network. The features from different levels are dynamically mixed and decoded to obtain a haze-free image. The experimental results show that the proposed algorithm has a good dehazing performance on indoor, outdoor, and real hazy images, and can generate clear and natural dehazing images. [ABSTRACT FROM AUTHOR]

Loop closure detection can eliminate the accumulated error of visual SLAM, which is of great significance to the SLAM system. Among them, the widely used visual bag-of-word model algorithm has the problem of visual word identity and ambiguity, which affects the loop closure detection effect. To improve these problems and loop back detection effect, this paper proposed a loop closure detection algorithm based on soft-assigned SIFT features. The algorithm allocated the SIFT feature points extracted from the image to several words with the closest Euclidean distance, according to the distance sorting and weighting, and removed the feature points that were far away from the words to generate more discriminating descriptors. In addition, when filtering candidates, the algorithm added the proportion constraint of the same word feature points and the stability constraint of word shift to filter out a few candidates. In the experimental results, the algorithm compared with the traditional visual bag-of-words model and several loop closure detection algorithms in recent years, the recall rate at 100% accuracy rate improved under three data sets, and the average image query time was about 40 ms. The results show that the algorithm can improve the loop detection effect and ensure the real-time performance. [ABSTRACT FROM AUTHOR]

In order to fix the integer ambiguity quickly and accurately, this paper proposed a new ambiguity search algorithm based on measurement accuracy constraints to solve the problems of the LAMBDA algorithm, such as the wide search range of the integer ambiguity and the low search efficiency. When the least squares problem got optimally weighted, the algorithm took the carrier phase measurement accuracy as the constraint condition to test the fixed ambiguity solution on the basis of the stan-dard differential evolution （DE） algorithm. This algorithm solved the influence of different satellite altitude angles, and could achieve 99% success rate when solving 3D integer ambiguity. Compared with the MLAMBDA algorithm, the standard DE algorithm and the adaptive weighted differential evolution （AWDE） algorithm, the proposed algorithm further improves the efficiency and success rate of ambiguity resolution. [ABSTRACT FROM AUTHOR]

Ambiguity resolution based on smartphone GNSS measurements can enable various potential applications that currently remain difficult due to ambiguity biases, especially under kinematic conditions. This study proposes an improved ambiguity resolution algorithm, which uses the search-and-shrink procedure coupled with the methods of the multi-epoch double-differenced residual test and the ambiguity majority tests for candidate vectors and ambiguities. By performing a static experiment with Xiaomi Mi 8, the AR efficiency of the proposed method is evaluated. Furthermore, a kinematic test with Google Pixel 5 verifies the effectiveness of the proposed method with improved positioning performance. In conclusion, centimeter-level smartphone positioning accuracy is achieved in both experiments, which is greatly improved compared with the float and traditional AR solutions. [ABSTRACT FROM AUTHOR]

This work presents a generalized iterative algorithm of short-term recovery management after large-scale disasters. The proposed method enables the coordination of reconstruction processes when the initial information is fluid and uncertain. The demonstrated algorithm supplies the recovery administration with a set of generated recovery plans, which may be compared from multiple perspectives. The city is modeled adopting the PCANS framework (Krackhardt et al., 1988), enabling to simulate of the complex interactions between individual recovery agents and recovery teams, recovery teams and individual infrastructure links, and between individual infrastructure links and the performance of a particular infrastructure or the whole city. The resulting model forming a virtual twin of an urban area is analyzed numerically with a set of different restoration strategies and comparing the efficiency of the resulting plans. As a result, the recovery coordinator is provided with a set of optional restoration strategies, which enables avoidance of the delay of recovery initiation caused by the ambiguity of the initial state of the system and the impossibility of defining an optimal recovery plan during the short-term recovery phase. [ABSTRACT FROM AUTHOR]

A new ensemble framework for an interpretable model called linear iterative feature embedding (LIFE) has been developed to achieve high prediction accuracy, easy interpretation, and efficient computation simultaneously. The LIFE algorithm is able to fit a wide single-hidden-layer neural network (NN) accurately with three steps: defining the subsets of a dataset by the linear projections of neural nodes, creating the features from multiple narrow single-hidden-layer NNs trained on the different subsets of the data, combining the features with a linear model. The theoretical rationale behind LIFE is also provided by the connection to the loss ambiguity decomposition of stack ensemble methods. Both simulation and empirical experiments confirm that LIFE consistently outperforms directly trained single-hidden-layer NNs and also outperforms many other benchmark models, including multilayers feed forward neural network (FFNN), Xgboost, and random forest (RF) in many experiments. As a wide single-hidden-layer NN, LIFE is intrinsically interpretable. Meanwhile, both variable importance and global main and interaction effects can be easily created and visualized. In addition, the parallel nature of the base learner building makes LIFE computationally efficient by leveraging parallel computing. [ABSTRACT FROM AUTHOR]

Reasonable urban commercial planning must clarify the location and scope of urban commercial districts (UCDs). However, existing studies typically detect spurious UCDs owing to the bias in a single data source while ignoring the continuity and ambiguity of commercial district boundaries. Therefore, in this study, we designed a two-stage approach for detecting UCDs. First, points of interest and population heat data were fused through hotspot and overlay analyses to detect core commercial areas. The boundaries of the UCDs were then identified by considering adjacent blocks using adjusted cosine similarity and region-growing algorithms. Finally, an experiment was conducted in Xiamen, revealing concentrated businesses on Xiamen Island and sparse businesses outside Xiamen Island. An experimental comparison with other strategies confirmed the improved modeling ability of this approach for the edge ambiguity of UCDs. This framework provides tools for urban commercial planning and helps recognize urban commercial patterns in a timely manner. [ABSTRACT FROM AUTHOR]

In the inversion of geophysical data, an attempt is made to obtain a model with the best fit on the observed data. Unfortunately, the results are usually accompanied by non-uniqueness and ambiguity. These inversion problems can be reduced by inverting different geophysical datasets. Sequential inversion is one of the most common ways to integrate two or more geophysical datasets, to obtain a model that is compatible with all geophysical data, thus reducing the amount of ambiguity. This paper presents separate inversions of DC resistivity and magnetic data and sequential inversion of DC resistivity constrained by magnetic data. Here, the inverse model of magnetic data is considered the initial model for the sequential inversion of DC resistivity data. At first, the algorithm is applied to a synthetic model composed of four conductive and magnetized bodies, and the results show notable improvement for the resistivity model after sequential inversion, compared with the separate resistivity inversion model. Finally, encouraged by the results obtained in the synthetic case, the algorithm was applied to DC resistivity and magnetic datasets that were collected in the archeological area of old Pompeii city nearby Naples, Italy. The result of the sequential resistivity inversion model was notably superior to the corresponding resistivity model obtained from standard separate inversion. [ABSTRACT FROM AUTHOR]

Non-rigid multi-part shape matching has proven to be essential and challenging in many applications. This paper analyzes the aforementioned problem and proposes a novel multi-part shape matching method to simultaneously compute correspondences between a full shape and its multiple parts undergoing a non-rigid deformation. The main idea is to simultaneously integrate the Hamiltonian eigenvalue equivalence strategy as a part regularization term being fully spectral with the partial functional map. Moreover, we introduce a new upsampling refinement approach based upon ZoomOut in conjunction with the regularized point-wise map recovery algorithm to obtain high-quality partial matches. Our method naturally handles various challenges and noise that commonly occur in real scans, like non-rigid deformations, strong partiality, topological noise, and symmetric ambiguity. Finally, we demonstrate superior qualitative and quantitative results on several datasets. We show that our method produces more accurate, smoother results than other competing methods in realistic scenarios. [ABSTRACT FROM AUTHOR]

This study introduces a new scheme for resolving the ambiguity in a pulse‐Doppler radar. While ambiguity resolution is typically performed before tracking, the proposed approach, Track‐Before‐Resolving, is resolving ambiguity utilising tracking filters. A specific Doppler ambiguity resolution algorithm is provided for demonstrating that the Track‐Before‐Resolving method is useful. In order to resolve the ambiguity, which is equivalent to finding the ambiguity number, this algorithm investigates the maximum probability calculated in the multiple model estimation, each with a tracking filter of a different ambiguity number. Simulation shows that the proposed algorithm performs effectively, even at low SNR. [ABSTRACT FROM AUTHOR]

As public administrations adopt artificial intelligence (AI), we see this transition has the potential to transform public service and public policies, by offering a rapid turnaround on decision making and service delivery. However, a recent series of criticisms have pointed to problematic aspects of mainstreaming AI systems in public administration, noting troubled outcomes in terms of justice and values. The argument supplied here is that any public administration adopting AI systems must consider and address ambiguities and uncertainties surrounding two key dimensions: the algorithms' results, and how public managers make decisions for and about AI systems' design. The article is based on bibliographic research in institutional theory perspective that relates the design of AI systems and relevant literature on the decision-making process in public policy. The main finding is to explain why autonomous decision systems continue to reproduce ambiguities and uncertainties when applied in public administration and propose a reflection on AI governance in public administration. This article points to the need for design institutions that immerse themselves in understanding the nuances, details, and potential outcomes of AI governance for public administration. Such institutions would reconcile consequentialist logic with a logic of appropriateness to help navigate and mediate ambiguities and uncertainties. [ABSTRACT FROM AUTHOR]

The speed and direction of a missile shifts sharply in the dive phase, making the azimuth frequency modulation (FM) rate change with the azimuthal position, leading to azimuth ambiguities and image distortion. To solve this problem, a modified nonlinear chirp scaling (NLCS) algorithm was adopted to compensate for the azimuth FM rate. First, the geometric configuration and echo signal model of the spaceborne missile bistatic synthetic aperture radar (SAR) were built, and then the Doppler frequency correction was performed, and the 2-D spectrum of the signal was derived by the method of series reversion. Next, range migration correction and range compression were finished in the 2-D frequency domain. Following this, a modified NLCS algorithm was proposed to solve the space variance of Doppler phase problem. After compensating for the azimuth FM rate, the azimuth compression focusing was completed and the imaging result was obtained. Finally, by comparing the calculation amount, imaging effect, and performance index with the traditional NLCS algorithm, it can be concluded that the algorithm reduced the calculation amount by 1.0128 × 108 floating point operations per second (FLOPs) compared with the traditional NLCS algorithm, and the azimuth focusing effect of the edge point was greatly improved. Its resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) were improved by 0.87 m, 3.32 dB, and 1.79 dB, respectively, which proved the effectiveness and feasibility of this method. [ABSTRACT FROM AUTHOR]

In computer-assisted systems, real-time instrument segmentation is an essential module, and real-time segmentation conducted against streaming data reaches into treasure data in real time. The major benefit of real-time segmentation is that it can be used to build categories based on new movements made by users on a website, such as those who are searching or coming for the first time. Furthermore, real-time segmentation and identification algorithms for wires in mobile terminals are extensively used and be useful and efficient in this process. In this study, we employed the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate real-time segmentation and identification methods. FAHP is a simple and intuitive method for calculating the weights of alternatives, criteria, and ranking them to identify the most efficient and effective solution. In this research, we have used the FAHP approach with fuzzy geometric mean values to rank six criteria and three alternatives. The FAHP approach is generally used in multi-decision-making situations where ambiguity and uncertainty are involved. It was tried to increase the context for the creative growth of real-time segmentation and identification algorithms for wires by evaluating these possibilities. [ABSTRACT FROM AUTHOR]

With the development and integration of GNSS systems in the world, the positioning accuracy and reliability of GNSS navigation services are increasing in various fields. Because the current multisystem fusion leads to an increase in the ambiguity dimension and the ambiguity parameters have discrete characteristics, the current conventional search algorithm leads to low search efficiency when the ambiguity dimension is large. Therefore, this paper describes a new algorithm that searches the optimal lattice points by lattice theory through the breadth-first algorithm and reduces the search space of ambiguity by calculating and judging the Euclidean distance between each search variable and the target one so as to propose a new lattice ambiguity search algorithm based on the breadth-first algorithm. The experimental results show that this method can effectively improve the search efficiency of ambiguity in high-dimension situations. [ABSTRACT FROM AUTHOR]

Multi-GNSS multi-frequency observations improve the accuracy, availability and reliability of the single-site RTK positioning but also simultaneously raise the communication bandwidth needed for the transmission of differential corrections. A RTK positioning algorithm is proposed that transmit the phase-only differential corrections to achieve the ambiguity-fixed RTK positioning, significantly reducing the communication bandwidth requirements. The essence of the presented method is that the code differential positioning solution implied in the conventional RTK positioning procedure can be replaced by the single point positioning solution of the rover receiver to realize RTK positioning. For typical application scenarios, the transmitted bits in a single epoch can be reduced by more than 25% when the corrections are sent with the RTCM protocol. The experiments by the GPS dual-frequency data over the 10 km and 180 km baselines shown that the performance of the presented RTK algorithm are comparable to that of the conventional RTK positioning, which verifies its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

With the increasing number of available satellites from multi‐global navigation satellite systems (GNSS) and their applications in complicated environments, an increased number of faults in measurements are inevitable. Fault detection and exclusion (FDE) is an effective way to reject observation faults in order to guarantee the integrity of a GNSS positioning and navigation system. We propose a modified between‐receiver single‐differencing (SD)‐based FDE algorithm for real‐time kinematic (RTK) positioning. First, a modified between‐receiver SD model‐based FDE is proposed for testing the estimated float ambiguities. This is helpful in rejecting observations with unreliable float ambiguities and obtaining reliable double‐differencing (DD) integer ambiguities, denoted as the SD float model in the sequel. Second, DD ambiguities are resolved by the DD model and used to update the previous SD float ambiguity. After that, a modified SD fix model, taking the DD ambiguities as the known parameters, is further developed to detect any unreliable or fault‐resolved ambiguities. To verify the modified SD model‐based FDE algorithm, a kinematic vehicle test is conducted. The fault detection test statistics of the SD float model and the SD fix model, time‐to‐first‐fix, ambiguity‐fix rate, SD residuals, and positioning performances are examined. The results show that the modified FDE method can detect and exclude fault satellites accurately and effectively, which is verified by the SD residuals of fault satellites. The ambiguity‐fix rate of the proposed modified FDE algorithm is improved by approximately 3% with 70 more epochs of fixes. Regarding the 3D positioning performance, the modified proposed SD‐based FDE algorithm can achieve 54.11%, 57.78%, and 73.11% improvements for standard deviation, root mean square, and mean bias, respectively, when compared with the processing strategy without the use of the proposed FDE. [ABSTRACT FROM AUTHOR]

To enable organizations to achieve carbon neutrality through agile capabilities, this paper establishes an integrative framework of carbon-zero agility consisting of three dimensions: search scope agility, search locus agility, and search pace agility. However, applying common agile methodologies like Scrum, Kanban, and Lean to cultivate these capabilities inevitably introduces feedback ambiguity, which can paralyze decision-making and increase errors due to inherent human cognitive limitations. To address this, tailored carbon-zero feedback algorithms are proposed to complement human judgment in agile workflows. Specifically, prescriptive analytics, federated learning, and probabilistic programming are injected into Scrum, Kanban, and Lean respectively to restore clarity amidst ambiguity. The framework is grounded in cases from the textile industry to demonstrate applicability in practical settings. By targeting the roots of distortions with human-algorithm collaborations, it provides an actionable roadmap to implement carbon-zero agility. • Establishes carbon-zero agility framework: search scope, locus, and pace agility. • Identifies behavioral challenges, and feedback ambiguity in agile for carbon-zero. • Proposes tailored carbon-zero feedback algorithms to enhance agility. • Connects agile processes with broader organizational sustainability capabilities. [ABSTRACT FROM AUTHOR]

In shadow detection area, deep learning‐based methods have achieved outstanding performance. However, the potential ambiguity in shadow images is still the main reason for the poor performance of the algorithms. By analysing the relationships among shadow edges, shadow false‐positive information, and shadow areas, an edge‐guided disambiguation module for better shadow detection performance is proposed. The edge‐guided disambiguation module consists two parts: 1) the shadow edge feature is used to guide the comprehensive shadow area feature generation of different resolution. 2) The shadow false‐positive features is applied to reduce ambiguity in the comprehensive shadow area features. In this way, the missed detections is added in the first step and the false detections is reduced in the second step. More accurate detection results is obtained. The comprehensive experiments are proceeded on the three public shadow detection datasets: SBU, UCF, and ISTD. The experimental results demonstrated the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Solving the phase ambiguity problem is crucial to achieving a wide-range and high-precision measurement for the frequency-domain sampling (FDS)-based surface acoustic wave (SAW) delay-line sensor systems. This study proposes an improved phase estimation algorithm called dual-band phase estimation (DBPE) to solve the problem. By using DBPE, the SAW sensor system can obtain an extensive and alterable measuring range without further requirements for sensor design or transmitted signals. Thus, it can be widely used in various FDS-based SAW delay-line sensor systems. Monte Carlo simulations and temperature measuring experiments, based on a YZ-cut LiNbO3 SAW delay-line sensor and a switched frequency-stepped continuous wave (S-FSCW) reader, are performed to demonstrate the algorithm’s validity. The Monte Carlo simulations show that DBPE can effectively solve the phase ambiguity problem and has better performance than frequency estimation in measuring precision at a low signal-to-noise ratio (SNR). The temperature-sensing experiments show that DBPE has a good performance in measuring range and precision, serving as a phase ambiguity solver in the temperature sensor system. [ABSTRACT FROM AUTHOR]

The sparse array has ambiguity characteristics, which makes the traditional ISM method unable to be directly applied when the amplitude and phase errors or position errors of the array elements exist. In view of this defect, the study utilizes the principle of DOA estimation algorithm based on medium defuzzification in linear arrays, extends this algorithm to two-dimensional sparse planar arrays, and deeply studies the influence of medium parameters on the suppression effect by introducing the concept of suppression degree. This study combines the algorithm with the classic ISM method to establish a new sparse planar array amplitude and phase error estimation algorithm. The simulation results show that this proposed method can not only make the blur angle suppression degree up to 17 dB under the condition of reasonable selection of media parameters, but also can estimate the amplitude and phase error under the premise of obtaining the accurate angle, which improves the robustness. [ABSTRACT FROM AUTHOR]

The problem of range estimation based on noisy measurements with phase ambiguity can be solved using signals across multiple frequencies by existing algorithms based on either the Chinese Remainder Theorem or lattice theory. The performances of these algorithms are constrained by the trade-off between SNR of measurements and required estimation accuracy. In this paper, we firstly propose an algorithm to find a set of wavelengths satisfying the conditions to apply the closed-form lattice algorithm from a given interval. Then we propose an enhanced lattice algorithm for range estimation based on existing lattice approaches. In the conventional lattice algorithm, the selection of an integer vector by the estimator often fails to be the ground truth because of noise. As a result, the true value can be a neighbouring lattice point of the estimated point where neighbours are defined in terms of a distance depending on the noise characteristics. In this paper, all neighbouring lattice points of the estimated point obtained by a conventional lattice algorithm are regarded as candidates for the new estimate. Then an efficient estimator is presented to choose one of those candidates to be the new estimate. The performance of the proposed algorithm, i.e. the successful probability of reconstruction, is derived mathematically. Additionally, a computation-reduced algorithm is presented to reduce the overhead of computation. The calculated probability and simulation results demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Introduction: Currently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness.Methods: This study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm.Results: We developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites.Discussion and Conclusion: Our findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms. [ABSTRACT FROM AUTHOR]

Current sentiment analysis models usually use word2vec, Glo Ve and other methods to generate static word embedding, and traditional convolutional or recurrent depth models cannot fully focus on the context, extract insufficiently features, and reduce the accuracy of sentiment judgment. This paper proposed a Chinese text sentiment analysis model based on ELMo and Bi-SAN. Firstly, through ELMo language model training, the model got the word vector that integrated the word itself and context information to solve the problem of ambiguity of a word. Meanwhile, it used pre-trained skip-gram algorithm to replace the embedding layer of the randomly initialized ELMo model and improved the convergence speed of the model. Then the model used Bi-SAN to extract features. Due to the self-attention mechanism, Bi-SAN could fully focus on the context of each word and extract features more comprehensively. Compared with multiple existing sentiment analysis models, the proposed model achieves higher F, in the hotel review dataset and the NLPCC2014 task2 Chinese dataset, which validates the effectiveness of the model. [ABSTRACT FROM AUTHOR]

This study uses a low-density solid-state flash lidar for estimating the trajectories of road vehicles in vehicle collision avoidance applications. Low-density flash lidars are inexpensive compared to the commonly used radars and point-cloud lidars, and have attracted the attention of vehicle manufacturers recently. However, tracking road vehicles using the sparse data provided by such sensors is challenging due to the few reflected measurement points obtained. In this paper, such challenges in the use of low-density flash lidars are identified and estimation algorithms to handle the same are presented. A method to use the amplitude information provided by the sensor for better localization of targets is evaluated using both physics-based simulations and experiments. A two-step hierarchical clustering algorithm is then employed to group multiple detections from a single object into one measurement, which is then associated with the corresponding object using a Joint Integrated Probabilistic Data Association (JIPDA) algorithm. A Kalman filter is used to estimate the longitudinal and lateral motion variables and the results are presented, which show that good tracking, especially in the lateral direction, can be achieved using the proposed algorithm despite the sparse measurements provided by the sensor. [ABSTRACT FROM AUTHOR]

The undifferenced ambiguity is recovered to the integer characteristics after the fractional cycle bias (FCB) product correction, which significantly shortens the convergence time of precision point positioning (PPP). When the uncombined FCB products are estimated, the original L1 and L2 FCB cannot be accurately separated due to the limitation of global ionospheric model accuracy.On one hand, due to the limitation of the accuracy of global ionospheric grid model, L1 and L2 ambiguity cannot be separated accurately in real‐time, which makes the inconsistent for user end using the uncombined FCB products. On the other hand, due to the difference between the ionospheric combined FCB products and the uncombined FCB products, the user end with uncombined PPP model cannot use the ionospheric FCB products. Methods：A partial ambiguity resolution method for uncombined PPP using the ionosphere‐free combined FCB product is proposed, which considers the consistency between the algorithm of generating the ionosphere‐ free combined at server end FCB product and the algorithm of the uncombined ambiguity resolution at user end. Constructing the narrow‐lane ambiguity by using the raw ambiguity and wide‐lane ambiguity of single difference between satellites, the ionosphere‐free combined FCB product is used to fix the ambiguity step by step. Consisting of about 120 global multi ‐ GNSS experiment （MGEX） stations are used to generate the ionosphere‐free combined FCB and uncombined FCB products, and 10 stations which are not in the service end are selected for evaluation and validation. For the server end with 120 MGEX stations, 97.3% of the wide‐lane ambiguity residuals and 96.8% of the narrow‐lane ambiguity residuals are distributed after ionosphere‐free combined FCB products correction. 96.7% of the wide‐lane ambiguity residuals and 97.7% of the narrow ‐ lane ambiguity residuals are distributed after uncombined FCB products correction. Results: The experimental results show that the positioning accuracy of the proposed method is improved by 25.0% and the convergence time is shortened by 21.1% in static condition, 26.7% and 17.9% in dynamic condition, respectively.Conclusions: Compared with the traditional FCB method, the proposed method can improve the positioning accuracy and shorten the convergence time, which can further broaden the application scenarios of PPP. [ABSTRACT FROM AUTHOR]

Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement. However, the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces, which gives the deconvolved result strong ambiguity and poor spatial continuity. To alleviate this issue, we developed a structurally constrained deconvolution algorithm. The proposed method extracts the reflection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial reflection regularization. Benefiting from the introduction of the reflection regularization, the proposed method enhances the stability and spatial continuity of conventional deconvolution methods. Synthetic and field data examples confirm the correctness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

A new algorithm named N‐BANDS has been developed for the estimation of the combined effect of noise and rotational ambiguity in the bilinear decomposition of data matrices using the popular multivariate curve resolution–alternating least‐squares model. It is based on a nonlinear maximization and minimization of a component‐wise signal contribution function (SCF), with a single‐objective function and a separate module for applying a variety of constraints. The algorithm can be applied to multicomponent systems and efficiently estimates the extreme component profiles corresponding to maximum and minimum SCF in the presence of varying amounts of instrumental noise. Simulated systems mimicking multicomponent and multisample analytical calibration protocols have been studied, having uncalibrated interferents in the test samples. Different noise structures (independent and identically distributed, proportional and correlated) were added to the data matrices, with results that indicate an increase in the extension of feasible bands as the noise level increases. N‐BANDS has been developed as a new algorithm for estimating the combined effect of noise and rotational ambiguity in bilinear decompositions using multivariate curve resolution–alternating least‐squares, based on the maximization/minimization of component‐wise signal contribution functions, with a single objective function and a separate module of constraints. Multi component and multi sample simulated systems were studied with uncalibrated interferents in test samples. Noise addition results indicate an increase in the extension of feasible bands as the noise level increases. [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

Larger array aperture is provided by sparse arrays than uniform ones, which can improve the angle estimation resolution and reduce the cost of system evidently. However, manifold ambiguity is introduced due to the array sparsity. In this paper, a Power Estimation Multiple-Signal Classification (PE-MUSIC) algorithm is proposed to solve the manifold ambiguity of arbitrary sparse arrays for uncorrelated sources in Multiple-Input Multiple-Output (MIMO) radar. First, the paired direction of departure (DOD) and direction of arrival (DOA) are obtained for all targets by MUSIC algorithm, including the true and spurious ones; then, the well-known Davidon–Fletcher–Powell (DFP) algorithm is applied to estimate all targets' power values, among which the value of a spurious target trends to zero. Therefore, the ambiguity of sparse array in MIMO radar can be cleared. Simulation results verify the effectiveness and feasibility of the method. [ABSTRACT FROM AUTHOR]

The azimuth phase coding (APC) technique is known for its very low implementation complexity and its effectiveness for point and distributed ambiguities in conventional synthetic aperture radar (SAR) systems. In recent years, as an extension, the APC technique has been briefly discussed for multichannel SAR systems. However, the properties of the APC technique are no longer guaranteed in the multichannel SAR systems based on the digital beamforming (DBF) on-receive, and only a slight APC gain in the suppression of the range ambiguity can be obtained. In this article, we first provide a more thorough analysis for an APC-multichannel SAR system with respect to a uniform pulse-repetition frequency (PRF). Then, the APC/multichannel SAR system with nonuniform spatial sampling is briefly discussed, and an improved reconstruction approach based on a quadratically constrained optimization model is proposed to increase greatly the APC gain with respect to existing multichannel reconstruction algorithms. This proposed approach allows the minimization of the range ambiguity with a given azimuth-ambiguity constraint. In particular, for some specific PRFs, the proposed method permits a cancellation of the odd-order range ambiguity. Finally, simulation experiments are performed to verify the advantages and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Range ambiguity is a significant challenge in conventional pulse-Doppler radar systems with medium or high pulse repetition frequencies, greatly impacting radar detection and imaging capabilities. The classic approaches such as waveform diversity and compressed sensing are based on the isolation between pulses. However, these methods encounter issues like residual ambiguity and grid mismatch, resulting in poor range ambiguity suppression performance. In this paper, the multiple signal classification-alternating projection (MUSIC-AP) method is proposed to address these challenges and achieve high-resolution estimation. The proposed method converts the range-velocity estimate into a spectrum estimate, enabling the utilization of the MUSIC algorithm to solve the grid mismatch problem and improve echo reconstruction. Then, to mitigate the effect of ambiguous energy on reconstruction, the MUSIC reconstruction subprocess is embedded into the AP framework. Through iterative approximation, the method gradually suppresses ambiguous and noise energy, thereby further enhancing the echo reconstruction accuracy and range ambiguity suppression performance. Simulations demonstrate the effectiveness of the proposed method. Moreover, algorithm analysis is conducted, considering aspects such as resolution, robustness, and sparsity, which provides a basis for parameter selection and applicability conditions. [Display omitted] • The range-velocity estimation is transformed into spectrum estimation problem. • Unambiguous range-velocity high-resolution estimation via the MUSIC algorithm. • The MUSIC is embedded into AP outer frame to gradually suppress ambiguity and noise. [ABSTRACT FROM AUTHOR]

In view of the problem that the JPDA algorithm can only track the known number of targets and association ambiguity, simultaneously, the number of hypotheses in MHT algorithm increases exponentially over time. This paper proposed a measurement-oriented m-optimal hypothesis N-scan MITT algorithm. Firstly, it used the method of generating m-optimal hypothesis to generate a specified number of optimal and sub-optimal hypothesis in each frame,and used fixed-value N of hypothesis tree depth to generate optimal feasible hypothesis to complete data association. This paper used two-point difference linear method and global least squares estimation to solve the track initiation of single new target and multiple new targets, respectively. The simulation results show that, comparing with MITT-DAM algorithm, the proposed method achieves a balance between tracking performance and operation time. [ABSTRACT FROM AUTHOR]

The analysis of reaction systems and their kinetic modeling is important for both exploratory research and process design. Multivariate curve resolution (MCR) methods are state-of-the-art tools for the analysis of spectral series, but are also affected by an unavoidable solution ambiguity that impacts the obtained concentration profiles, spectra and model parameters. These uncertainties depend on the underlying model and the magnitude of the measurement perturbations. We present a general theoretical approach together with a computational method for the analysis of the solution ambiguity underlying arbitrary kinetic models. The main idea is to determine all those model parameters for which the corresponding pure component factorizations satisfy all given constraints within small error tolerances. This makes it possible to determine bands of concentration profiles and spectra that reflect the underlying ambiguity and circumscribes the potential reliability of MCR solutions. False conclusions on the uniqueness of a solution can be prevented. The procedure can be applied as a post-processing step to MCR methods as MCR-ALS, ReactLab or others. The Matlab program code is freely accessible and includes not only the proposed ambiguity analysis but also an MCR hard-modeling approach. Application studies are presented for two experimental data sets, namely for UV/Vis spectra on the relaxation of a photoexcited state of benzophenone and for Raman spectra on an aldehyde formation process. Image 1 • The parameter ambiguities of general kinetic models are analyzed. • A novel freely available algorithm is introduced for their computation. • The results are applied to UV/Vis and Raman spectroscopic data. [ABSTRACT FROM AUTHOR]

To produce more precise descriptions of air–sea exchanges under tropical cyclones (TCs), spaceborne synthetic aperture radar (SAR) instruments provide unique capabilities to probe the ocean surface conditions, at very high spatial resolution, and on synoptic scales. Using highly resolved (3 km) wind fields, an extensive database is constructed from RadarSat-2 and Sentinel-1 SAR acquisitions. Spanning 161 tropical cyclones, the database covers all TC intensity categories that have occurred in 5 different TC basins, and include 29 cases coincident with SFMR measurements. After locating the TC center, a specific methodology is applied to filter out areas contaminated by heavy precipitation to help extract, for each acquisition, the maximum wind speed (Vmax), its associated radius (Rmax), and corresponding outer wind radii (R34/50/64 kt). These parameters are then systematically compared with best track (BTK), and when available, SFMR airborne measurements. For collocated SFMR and SAR observations, comparisons yield root-mean-squares of 3.86 m s-1 and 3 km for ocean surface wind speeds and TC Rmax, respectively. High correlations remain for category-5 cases, with Vmax exceeding 60 m s-1. The largest discrepancies are found between BTK and SAR Rmax estimates, with Rmax fluctuations poorly captured by BTK, especially for rapidly evolving category-3, -4, and -5 TCs. In heavy precipitation (>35 mm h-1), the SAR C-band measurements may be impacted, with local ambiguities associated with rain features, as revealed by external rain measurements. Still, this large dataset demonstrates that SAR measurements have unique high-resolution capabilities, capturing the inner- and outer-core radial structure of the TC vortex, and provide independent and complementary measurements than those used for BTK estimates. [ABSTRACT FROM AUTHOR]

Ambiguous sentences are divided into 3 types namely phonetic, lexical, and grammatical. This study focuses on grammatical ambiguous sentences, grammatical ambiguous sentences are ambiguities that occur due to incorrect grammar, but this ambiguity will disappear once it is used within a sentence. Ambiguous sentences become a big problem when they are processed by a computer. In order for the computer to interpret ambiguous words correctly, this study seeks to develop detection of Indonesian ammbiguous sentences using Boyer Moore algorithm. This algorithm matches ambiguous sentences that are inserted as input with the data set. Then the sentence is being detected whether it contains ambiguous sentences, by calculating the percentage of similarity using cosine similarity method. Cosine similarity system is able to find out the meaning of the sentence. In the data set, the number of ambiguous sentences that can be collected is 50 words. The 50 words consist of ambiguous words data, ambiguous sentences, and ambiguous sentence meanings. This system trial was carried out for 200 times and the accuracy level was 0.935, precision was 0.9320, and Recall was 0.8. While the F-Measure was 0.8061. While the speed for word search 0.003275 seconds [ABSTRACT FROM AUTHOR]

The piecewise constant Doppler (PCD) algorithm is a novel radar imaging process recently proposed for the generalized continuous-wave synthetic aperture radar (GCW-SAR). This article presents a detailed theoretical analysis on the PCD algorithm's performance and proposes a further complexity-reduced PCD algorithm with motion compensation (MOCO) suitable for practical applications. First, the difference between conventional SAR imaging and PCD imaging, i.e., the zeroth-order versus the first-order slant range approximation, is revealed. Exact ambiguity function expressions of the PCD imaging in range and azimuth directions, respectively, are then derived. An error function of the PCD imaging as compared with the ideal matched filtering method is further defined and shown to be a function of an image quality factor, which can be used to quantify the PCD imaging performance. Finally, a faster and more flexible imaging process, called decimated PCD algorithm, is proposed, by which the image azimuth spacing can be easily extended, and hence, the computational complexity can be significantly reduced. The decimated PCD implementation incorporated with the MOCO is developed for practical GCW-SAR applications, and its imaging error lower-bounded by the PCD imaging error function is analyzed accordingly. Simulation and experimental results validate the theoretical analysis of the PCD imaging and show that the decimated PCD algorithm can achieve a high imaging quality at low cost. [ABSTRACT FROM AUTHOR]

Topology design is a critical task for the reliability, economic operation, and resilience of distribution systems. This paper proposes a distributionally robust optimization (DRO) model for designing the topology of a new distribution system facing random contingencies (e.g., imposed by natural disasters). The proposed DRO model optimally configures the network topology and integrates distributed generation to effectively meet the loads. Moreover, we take into account the uncertainty of contingency. Using the moment information of distribution line failures, we construct an ambiguity set of the contingency probability distribution, and minimize the expected amount of load shedding with regard to the worst-case distribution within the ambiguity set. As compared with a classical robust optimization model, the DRO model explicitly considers the contingency uncertainty and so provides a less conservative configuration, yielding a better out-of-sample performance. We recast the proposed model to facilitate the column-and-constraint generation algorithm. We demonstrate the out-of-sample performance of the proposed approach in numerical case studies. [ABSTRACT FROM AUTHOR]

This article considers the dual-use unimodular signal design for a novel dual-functional radar-communication (DFRC) architecture. The information of downlink communication is modulated via the ambiguity function (AF) sidelobe nulling in the prescribed range-Doppler cells. At the same time, the sidelobe around the AF mainlobe is suppressed to ensure the radar detection performance. User equipment (UE) computes the AF of the received signal to make a judgment of the position of nulling for achieving demodulation. To this end, an objective function to evaluate the depth of nulling in AF is developed as figure of merit accounting for the worst-case sidelobe level ratio over the range-Doppler cells of interest. The resultant design is very challenging to solve due to the non-convex and non-smooth quartic fractional objective function and the NP-hard constant modulus constraint. Herein, we develop a fractional-alternating direction penalty method (FADPM) algorithm that invokes the fractional program theory and the ADPM framework. Specifically, we formulate a new ADPM form through introducing an auxiliary variable and resorting to the Dinkelbach's procedure. In each iteration, we transform the original quartic fractional program into two quadratic optimization subproblems both of which are approximated successively through a series of convex subproblems. We also provide the analytical convergence guarantee of the proposed FADPM algorithm. The simulation results verify the performance of the proposed algorithm and exhibit the effectiveness of the DFRC framework in modulation and demodulation process guaranteeing that a high data rate is achievable. [ABSTRACT FROM AUTHOR]

Objective: The uncertainty of influencing factors of BDS positioning is great in the disaster environment and emergency scene. The applicability and maneuverability of receiver in disaster emergency environment may not meet the needs of positioning. Therefore, the method of augmented positioning between long range stations on smart device was researched to meet the demand high-precision positioning in disaster emergency environment. The augmented positioning algorithm is convenient for high efficiency and easy to realize, and the user can use a very flexible data processing method. Methods: The un-difference error corrections between long-range reference stations was used of augmented positioning. The satellite clock error was eliminated and the atmospheric error and satellite orbit error can be weakened by the un-difference error corrections. The augmented positioning of user including pseudo-range, carrier phase and carrier phase smoothing pseudo-range between long range stations were achieved. And the demand of high-precision positioning of long range was meted by corrections of classified errors. The method of carrier phase smoothing pseudo-range by un-difference error corrections was used. The integer ambiguity was estimated by observations with eliminating errors and the pseudo-range was recalculated by the simple smoothing formula. The user station errors were removed or corrected by the reference station smoothing error corrections.The pseudo-range observations do not involve ambiguity with simple positioning mode and high- real-time performance, at the same time the accuracy of pseudo-range positioning can be improved by carrier phase observations. And this method can be better applied to the demand for high-precision positioning in disaster environment. The augmented positioning for specific receiver equipment was also integrated in the smart device. Results: Three reference stations between long range together with smart device and receiver in North China were used to test the augmented positioning. The positioning accuracy of dm or cm can be obtained by augmented positioning with pseudo-range and carrier phase smoothing of receiver, the accuracy on cm level obtained with carrier phase. The smart device can get the position on dm level of horizontal with carrier phase smoothing augmented positioning, and the augmented positioning with carrier phase was able to achieve the accuracy on cm level by fixed ambiguity or float ambiguity. The kinematic augmented positioning of smart device can get the positioning accuracy better than 1 meter with the test at Liaoning. Conclusions: The smart device can be used as the source of observation and the carrier of data processing for user receiver positioning. The Results: indicate that the method can achieve high-precision positioning with real-time dynamic centimetre, decimetre and sub-meter levels better than 1 meter. [ABSTRACT FROM AUTHOR]

Ionospheric disturbances are the phenomena which adversely affect the performance of precise positioning. This holds true even for multi-constellation relative positioning supported with network-derived ionospheric corrections. In such scenario the unfavorable effect is caused by a poor accuracy of corrections, which, in turn, is driven by the deterioration of the spatial interpolation process. The positioning becomes even more challenging in a wide-area scenario with baselines over 100 km. In this paper, we assess the methodology which aims at reliable and accurate wide-area RTK and rapid static positioning in the presence of severe ionospheric conditions. The approach takes advantage of multi-constellation network ionospheric corrections and an algorithm which allows the elimination of the temporal variations of the ionospheric delay. The experimental evaluation was performed on the basis of multi-station RTK and static positioning using GPS, BDS and Galileo data collected at high latitudes during the ionospheric storm on August 25–26, 2018. The results confirmed the deterioration of the accuracy of the network ionospheric corrections and consequently a decline in the positioning performance with routine models such as ionosphere-float and ionosphere-weighted. On the other hand, the results obtained with the application of the developed methodology demonstrated a very distinctive improvement in the ambiguity resolution domain and thus proved the advantage over benchmark models. In this case, the developed methodology allowed up to 20% enhancement of the ambiguity success rate with respect to benchmark strategies. [ABSTRACT FROM AUTHOR]

While the two-dimensional (2D) spectral peak search suffers from expensive computational burden in direction of arrival (DOA) estimation, we propose a reduced-dimensional root-MUSIC (RD-Root-MUSIC) algorithm for 2D DOA estimation with coprime planar array (CPA), which is computationally efficient and ambiguity-free. Different from the conventional 2D DOA estimation algorithms based on subarray decomposition, we exploit the received data of the two subarrays jointly by mapping CPA to the full array of the CPA (FCPA), which contributes to the enhanced degrees of freedom (DOFs) and improved estimation performance. In addition, due to the ambiguity-free characteristic of the FCPA, the extra ambiguity elimination operation can be avoided. Furthermore, we convert the 2D spectral search process into 1D polynomial rooting via reduced-dimension transformation, which substantially reduces the computational complexity while preserving the estimation accuracy. Finally, numerical simulations demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]