Navigation Applications Research Articles

Design and validation of a miniaturized reconfigurable power divider with arbitrary power split ratio and flexible output phase difference

This study introduces a microstrip line (ML) based reconfigurable 5-port power divider (PD) capable of manipulating power ratios at four output ports. The proposed PD offers two operational modes: unidirectional and bidirectional. In unidirectional transmission mode, the entire source power is directed to a particular output port, while in bidirectional transmission mode, the source power is equally distributed between two output ports. The selection of different transmission modes is accomplished by configuring six adaptive loads that are coupled to the PD structure, and the corresponding S-parameters of the proposed PD are derived by even–odd mode modeling. The resulting data is subsequently optimized by a numerical approach to identify the required design parameters that would ensure optimal performance. The proposed PD resonates at 1.06GHz and covers a frequency range spanning 0.9GHz to 1.2GHz, making it an excellent option for supplying regulated power to multi-port antennas or multiple input multiple output (MIMO) antennas proposed for aircraft navigation applications. The prototype has been developed to validate the notion of reconfigurable reactive loads utilizing varactor diodes and the recorded result is evaluated with the simulated results. The findings confirm excellent performance with regards to insertion loss (with a difference of <-2dB from the optimal values), reflection coefficient (≥-20dB), and isolation (≥-40dB) between the transmitting (input port) and non-transmitting or the remaining ports.

MOVES: Movable and moving LiDAR scene segmentation in label-free settings using static reconstruction

Accurate static structure reconstruction and segmentation of non-stationary objects is of vital importance for autonomous navigation applications. These applications assume a LiDAR scan to consist of only static structures. In the real world however, LiDAR scans consist of non-stationary dynamic structures — moving and movable objects. Current solutions use segmentation information to isolate and remove moving structures from LiDAR scan. This strategy fails in several important use-cases where segmentation information is not available. In such scenarios, moving objects and objects with high uncertainty in their motion i.e. movable objects, may escape detection. This violates the above assumption. We present MOVES, a novel GAN based adversarial model that segments out moving as well as movable objects in the absence of segmentation information. We achieve this by accurately transforming a dynamic LiDAR scan to its corresponding static scan. This is obtained by replacing dynamic objects and corresponding occlusions with static structures which were occluded by dynamic objects. We leverage corresponding static-dynamic LiDAR pairs. We design a novel discriminator, coupled with a contrastive loss on a smartly selected LiDAR scan triplet. For datasets lacking paired information, we propose MOVES-MMD that integrates Unsupervised Domain Adaptation into the network. We perform rigorous experiments to demonstrate state of the art dynamic to static translation performance on a sparse real world industrial dataset, an urban and a simulated dataset. MOVES also segments out movable and moving objects without using segmentation information. Without utilizing segmentation labels, MOVES performs better than segmentation based navigation baseline in highly dynamic and long LiDAR sequences. The code is available here.

Enhanced Infrared Vision by Nonlinear Up-Conversion in Nonlocal Metasurfaces.

The ability to detect and image short-wave infrared light has important applications in surveillance, autonomous navigation, and biological imaging. However, the current infrared imaging technologies often pose challenges due to large footprint, large thermal noise and inability to augment infrared and visible imaging. Here, infrared imaging is demonstrated by nonlinear up-conversion to the visible in an ultra-compact, high-quality-factor lithium niobate resonant metasurface. Images with high conversion efficiency and resolution quality are obtained despite the strong nonlocality of the metasurface. The possibility of edge-detection image processing augmented with direct up-conversion imaging for advanced night vision applications is furthershown.

Application Research of 5G Communication Technology in Satellite Navigation and Positioning

With the rapid development of 5G communication technology and the increasing perfection of global satellite navigation and positioning, the application of 5G communication technology in satellite navigation and positioning is particularly important. Based on the overview of 5G communication technology and satellite navigation and positioning, this paper analyzes the important value of 5G communication technology in satellite navigation and positioning, and then discusses the application of 5G communication technology and satellite navigation and positioning, aiming to integrate 5G communication technology, further improve the performance of satellite navigation and positioning system, and expand its application scenarios. It will bring greater convenience and benefits to social development.

The Parking Management System

This Parking Management System (PMS) is a comprehensive solution designed to streamline and enhance the efficiency of parking operations. Leveraging advanced technology, the system optimizes the utilization of parking spaces, reduces congestion, and improves overall user experience. Key features include real-time occupancy monitoring, automated payment processing, and smart allocation of available spaces. PMS not only ensures seamless entry and exit for vehicles but also integrates with mobile applications for convenient reservation and navigation. With a focus on sustainability, the system promotes eco-friendly practices by encouraging the use of electric vehicle charging stations and implementing intelligent traffic flow management.In summary, the Parking Management System represents a modern, user-friendly approach to address the challenges associated with urban parking, promoting a more organized and environmentally conscious urban environment. Keywords—Real-time Occupancy Monitoring, Automated Payment Processing

Review Paper: Key Points on Robot Navigation and Its Practical Uses in the Field of Manufacturing

Key aspects are covered in this review article, along with a brief explanation of mobile robot navigation and its uses in the industrial sector. This study emphasizes the significance of robot navigation in enhancing productivity, efficiency, and safety in manufacturing processes by compiling important ideas from the body of research and literature. This study investigates several robot navigation algorithms and strategies, from simple algorithms to sophisticated ones like SLAM (Simultaneous Localization and Mapping). This study also examines particular issues and concerns about the application of robot navigation systems in industrial settings, such as path planning, obstacle avoidance, and worker cooperation. This paper presents some noted applications of robot navigation, such as material handling, assembly, quality control, and logistics, using case studies and examples. The conversation also touches on new developments and prospective paths for robot navigation technology, highlighting the possibility for more innovation and connection with Industry 4.0 projects. All things considered, this review article is an invaluable tool for scholars, practitioners, and business experts who want to comprehend the function of robot navigation in contemporary manufacturing processes and how it will affect industrial automation in the future.

Simulated augmented reality-based calibration of optical see-through head mound display for surgical navigation.

Calibration of an optical see-through head-mounted display is critical for augmented reality-based surgical navigation. While conventional methods have advanced, calibration errors remain significant. Moreover, prior research has focused primarily on calibration accuracy and procedure, neglecting the impact on the overall surgical navigation system. Consequently, these enhancements do not necessarily translate to accurate augmented reality in the optical see-through head mount due to systemic errors, including those in calibration. This study introduces a simulated augmented reality-based calibration to address these issues. By replicating the augmented reality that appeared in the optical see-through head mount, the method achieves calibration that compensates for augmented reality errors, thereby reducing them. The process involves two distinct calibration approaches, followed by adjusting the transformation matrix to minimize displacement in the simulated augmented reality. The efficacy of this method was assessed through two accuracy evaluations: registration accuracy and augmented reality accuracy. Experimental results showed an average translational error of 2.14mm and rotational error of 1.06° across axes in both approaches. Additionally, augmented reality accuracy, measured by the overlay regions' ratio, increased to approximately 95%. These findings confirm the enhancement in both calibration and augmented reality accuracy with the proposed method. The study presents a calibration method using simulated augmented reality, which minimizes augmented reality errors. This approach, requiring minimal manual intervention, offers a more robust and precise calibration technique for augmented reality applications in surgical navigation.

Learning management systems for higher education: a brief comparison

The learning management system (LMS) is a software based on SAAS, internet navigator, or user application, which manage the teaching and learning of an academic or non-academic program. This work presents a comparison of 45 LMSs. The objective of this research is to report a study of the LMS developed for higher education. This objective is addressed by considering all LMSs with online access to information. For the comparison study, an evaluation criteria has been developed based on the literature review from the past 20 years. This evaluation methodology is based on Software Quality and Teaching-Learning tools for online educational platforms (SQTL), it is composed by six criteria: interoperability, accessibility, productivity tools, communication tools, learning tools, finally with the security standards and certifications. The maximum sum of SQTL score is 10 points and the minimum 0. The best LMS according to the comparison are Paradiso and Moodle with 9.50 and 9.25 points, respectively. Paradiso has around 1 million users and Moodle over 250 millions. The Google Sheets with the collected raw data and SQTL scores are available in https://shorturl.at/wqDWk.

“Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud”

The advent of autonomous mobile robots has spurred research into efficient trajectory planning methods, particularly in dynamic environments with varied obstacles. This study focuses on optimizing trajectory planning for an Autonomous Ground Vehicle (AGV) using a novel Reinforcement Learning Particle Swarm Optimization (RLPSO) algorithm. Real-time mobile robot localization and map generation are introduced through the utilization of the Hector-SLAM algorithm within the Robot Operating System (ROS) platform, resulting in the creation of a binary occupancy grid. The present research thoroughly investigates the performance of the RLPSO algorithm, juxtaposed against five established Particle Swarm Optimization (PSO) variants, within the context of four distinct physical environments. The experimental design is tailored to emulate real-world scenarios, encompassing a spectrum of challenges posed by static and dynamic obstacles. The AGV, equipped with LiDAR sensors, navigates through diverse environments characterized by obstacles of different geometries. The RLPSO algorithm dynamically adapts its strategies based on feedback, enabling adaptable trajectory planning while effectively avoiding obstacles. Numerical results obtained from extensive experimentation highlight the algorithm's efficacy. The navigational model's validation is achieved within a MATLAB 2D virtual environment, employing 2D Lidar mapping point data. Transitioning to physical experiments with an AGV, RLPSO continues to demonstrate superior performance, showcasing its potential for real-world applications in autonomous navigation. On average, RLPSO achieves a 10–15 % reduction in path distances and traversal time compared to the following best-performing PSO variant across diverse scenarios. The adaptive nature of RLPSO, informed by feedback from the environment, distinguishes it as a promising solution for autonomous navigation in dynamic settings, with implications for practical implementation in real-world scenarios.

Signal design and performance analysis for LEO high dynamic navigation application

Abstract With the development of GNSS (Global Navigation Satellite System), LEO (Low Earth Orbit) systems are adopted to enhance the system performance of GNSS. The signal Doppler of the LEO satellite is seven to nine times that of GNSS signals, which benefits positioning performance but leads to high acquisition complexity. This paper proposes the combination of a CSS (Chirp Spread Spectrum) marker and the main body of traditional modulation methods for high dynamic application. The acquisition calculation complexity and mean acquisition time of the proposed signal are analysed and compared with the traditional signal. The result shows that the acquisition calculation complexity is just 26 % of the traditional signal under the parameters considered and the mean acquisition time of the proposed signal is also lower than the traditional signal. Hence, the proposed signal is able to decrease the mean acquisition time of the receiver under the constraint of calculation complexity and should be adopted for LEO high dynamic application.

Campus Navigation using Augmented Reality and Virtual Reality

With the rapid advancement of Augmented Reality (AR) and Virtual Reality (VR) technologies, there has been a growing interest in leveraging these immersive technologies for enhancing campus navigation experiences. This paper presents a research study focused on designing and implementing a campus navigation system for the Maharashtra Institute of Technology World Peace University (MIT WPU) using AR/VR technologies. The proposed system aims to provide students, faculty, and visitors with an intuitive and immersive wayfinding experience, facilitating navigation across the expansive campus grounds. The core features of the proposed campus navigation system include real-time location tracking, interactive campus maps, personalized route planning, and integration with campus services and facilities information. The AR component overlays digital navigation cues and points of interest onto the user's physical surroundings, enhancing spatial awareness and providing contextual information. The VR component offers an immersive 3D visualization of the campus environment, allowing users to explore virtual replicas of buildings, landmarks, and amenities. The research methodology involves a multi-stage process, beginning with requirements gathering through stakeholder interviews and user surveys to understand the navigation challenges faced by campus users. Based on the gathered requirements, a prototype AR/VR campus navigation application is developed, leveraging state-of-the-art AR/VR development tools and technologies

DGVINS: Tightly Coupled Differential GNSS/Visual/Inertial for Robust Positioning Based on Optimization Approach

Abstract Due to the fragility of single-sensor positioning technology in complex scenarios, especially in complex urban areas, multi-sensor positioning technology is becoming increasingly popular. To further improve the robustness of the positioning system by fully utilizing the information from various sensors, this article proposes a differential-GNSS-visual-inertial navigation system (DGVINS) that tightly fuses differential global navigation satellite system (GNSS), vision and inertial information to provide accurate, robust and seamless position information for intelligent navigation applications. DGVINS effectively utilizes all sensor measurements within the factor graph optimization (FGO) framework. When using the carrier phase of GNSS, single-epoch ambiguity optimization is employed to prevent cycle slip detection and adapted to complex environments. We conducted experiments on public datasets with various features and compared the performance of simple differential-GNSS (DGNSS), DGNSS+Inertial, and the state-of-the-art GNSS-visual-inertial navigation systems (GVINS). We also compared the performance of different combinations of GNSS differential factors in various environments. Due to the superiority of differential GNSS and its appropriate integration with visual and inertial measurements, the experimental results demonstrate that DGVINS exhibits significant improvements in accuracy, stability, and continuity in both GNSS-challenged and vision-challenged environments.

Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style

This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions’ transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis.

Indoor Navigation in Facilities with Repetitive Structures.

Most facilities are structured in a repetitive manner. In this paper, we propose an algorithm and its partial implementation for a cellular guide in such facilities without GPS use. The complete system is based on iBeacons-like components, which operate on BLE technology, and their integration into a navigation application. We assume that the user's location is determined with sufficient accuracy. Our main goal revolves around leveraging the repetitive structure of the given facility to optimize navigation in terms of storage requirements, energy efficiency in the cellular device, algorithmic complexity, and other aspects. To the best of our knowledge, there is no prior experience in addressing this specific aim. In order to provide high performance in real time, we rely on optimal saving and the use of pre-calculated and stored navigation sub-routes. Our implementation seamlessly integrates iBeacon communications, a pre-defined indoor map, diverse data structures for efficient information storage, and a user interface, all working cohesively under a single supervision. Each module can be considered, developed, and improved independently. The approach is mainly directed to places, such as passenger ships, hotels, colleges, and so on. Because of the fact that there are "replicated" parts on different floors, stored once and used for multiple routes, we reduce the amount of information that must be stored, thus helping to reduce memory usage and as a result, yielding a better running time and energy consumption.

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art.

Fluctuations in Refracted Star Signals Caused by the Stratospheric Internal Gravity Waves

The application of starlight refraction navigation to spacecraft and space weapons is a significant development. However, the irregular stratospheric atmosphere can cause fluctuations in relative light intensity and refraction angles of refracted stars, which need to be analyzed to provide guidance for system design and simulation verification. The internal gravity wave (IGW) is an important component of the irregular atmosphere. Based on the Rytov approximation, closed-form approximations were obtained, which can more intuitively reveal the relationship between the IGW parameters and the star signals’ statistical characteristics. From the GOMOS observations, the influence of the stratosphere from 25 km to 35 km on the fluctuations in relative intensity and refraction angles was analyzed in this study. As the height increased, the fluctuations in starlight signals gradually weakened. Compared with the numerical solution, the error of the closed-form approximations for relative intensity fluctuations was no more than 10%, and the error for refraction angle fluctuations was 1.0%. Compared with the measured data, the error of the closed-form approximations for relative intensity was 6.3%. The proposed approximations better reflect the relationship between IGW parameters and star signal fluctuations compared to the existing approximation. The research in this article can provide a reference for application assessment based on starlight refraction navigation.

A weight induced contrast map for infrared and visible image fusion

Fusion involves merging details from both infrared (IR) and visible images to generate a unified composite image that offers richer and more valuable information than either individual image. Surveillance, navigation, remote sensing, and military applications require various imaging modalities, including visible and IR, to oversee specific scenes. These sensors provide supplementary data and improve situational understanding, so it is essential to fuse this information into a single image. Fusing IR and visible images presents several challenges due to the differences in imaging modalities, data characteristics, and the need for accurate and meaningful integration of information. In this context, a novel image fusion architecture focuses on enhancing prominent targets, with the objective of integrating thermal information from infrared images into visible images while preserving textural details within the visible images. Initially, in the proposed algorithm, the images from different sensors are divided into components of high and low frequencies using a Guided filter and an Average filter, respectively. A unique contrast detection mechanism is proposed that is capable of preserving the contrast information from the original images. Further, the contrast details of the IR and visible images are enhanced using local standard deviation filtering and local range filtering, respectively. We have developed a new weight map construction strategy that can effectively preserve the supplemental data from both the original images. These weights and gradient details of the source images are utilized to preserve the salient feature details of the images acquired from the various modalities. A decision-making approach is utilized among the high-frequency components of the original images to retain the prominent feature details of the source images. Finally, the salient feature details and the prominent feature details are integrated to generate the fused image. The developed technique is validated using both subjective and quantitative perspectives. The developed approaches provide EN, MI, Nabf, and SD of 6.86815, 13.73269, 0.15390, and 78.16158 respectively against deep learning-based approaches. Also, the proposed algorithm provides EN, MI, Nabf, FMIw, and Qabf against 6.86815, 13.73269, 0.15390, 0.41634 and 0.47196 respectively against existing traditional fusion methods. It is observed that the developed technique provides adequate accuracy against twenty-seven state-of-the-art techniques.

Can navigation applications facilitate equity in geographical access to emergency obstetric care?

Can navigation applications facilitate equity in geographical access to emergency obstetric care?

An evaluation of the positioning ability of the real-time ionosphere-free single-frequency precise point positioning technique in the Arctic and Antarctic polar regions under different ionospheric scintillations

This contribution presents an evaluation of the positioning ability of the real-time (RT) ionosphere-free (IF) single-frequency precise point positioning (SF-PPP) technique in Arctic and Antarctic under different auroral activities. Firstly, Global Positioning System (GPS)-only observations obtained from International GNSS Service (IGS) stations in the Arctic were evaluated with RT IF SF-PPP under all possible auroral activities. Then, a series of kinematic experiments were conducted using a zodiac boat on Livingston Island, located on the Antarctic Peninsula, under different auroral activities. Observations from the kinematic experiments were also evaluated using the RT IF SF-PPP. The outcomes indicate that the IF SF-PPP approach is not affected by ionospheric activities. Furthermore, the results reveal that the RT IF SF-PPP provides dm- and m-level positioning accuracy for horizontal and vertical components, respectively. The findings demonstrate that the RT IF SF-PPP technique can be reliably employed in real-time navigation applications in the Arctic and Antarctic.

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method.

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.