Navigation Information Research Articles

Interspecies-chimera machine vision with polarimetry for real-time navigation and anti-glare pattern recognition

Cutting-edge humanoid machine vision merely mimics human systems and lacks polarimetric functionalities that convey the information of navigation and authentic images. Interspecies-chimera vision reserving multiple hosts’ capacities will lead to advanced machine vision. However, implementing the visual functions of multiple species (human and non-human) in one optoelectronic device is still elusive. Here, we develop an optically-controlled polarimetry memtransistor based on a van der Waals heterostructure (ReS2/GeSe2). The device provides polarization sensitivity, nonvolatility, and positive/negative photoconductance simultaneously. The polarimetric measurement can identify celestial polarizations for real-time navigation like a honeybee. Meanwhile, cognitive tasks can be completed like a human by sensing, memory, and synaptic functions. Particularly, the anti-glare recognition with polarimetry saves an order of magnitude energy compared to the traditional humanoid counterpart. This technique promotes the concept of interspecies-chimera visual systems that will leverage the advances of autonomous vehicles, medical diagnoses, intelligent robotics, etc.

Differential-geometry-based multi-dimensional joint position-velocity estimation using Crab pulsar profile distortion

The traditional orbit determination method based on pulsar profile distortion can determine the six elements of the orbit. However, the estimation accuracies of these methods are limited and the computational load of a six-dimensional search is huge. To solve this problem, the differential-geometry-based Multi-dimensional Joint Position-Velocity Estimation (MJPVE) using Crab pulsar profile distortion is proposed in this paper. Firstly, through theoretical analysis, it is found that the pulsar profile distortion caused by the initial state error in some joint position-velocity directions is very small. In other words, the accuracies of estimation in these directions are very low. Namely, the search dimension can be reduced, which in turn greatly reduces the computational load. Then, we construct the chi-squared function of the pulsar profile with respect to the estimation error in joint position-velocity direction and use differential geometry to find the joint position-velocity directions corresponding to different degrees of distortion. Finally, we utilize the grid search based on directory folding in these joint position-velocity directions corresponding to large degrees of distortion to obtain the joint position-velocity estimation. The experimental results show that compared with the grouping bi-chi-squared inversion method, MJPVE has high precision and extensive navigation information.

Research on collaborative multi-UAV localization method based on combination navigation information

In challenging environments, unmanned aerial vehicle (UAV) systems often encounter unstable satellite signals and communication link interference. This paper proposes an integrated navigation method that integrates inertial navigation system (INS), global navigation satellite system (GNSS), and visual navigation system (VNS). Utilizing data from onboard sensors, this method merges relative navigation information from feature tracking of multiple UAVs with each UAV’s absolute navigation data. It includes specially designed transmission rules to reduce data exchange between UAVs. Each UAV uses an adaptive unscented Kalman filter (AUKF) method, which is enhanced into a collaborative AUKF (C-AUKF) using a message passing-based approach. Experiments in a simulated mission scenario revealed that the C-AUKF, in comparison to using extended Kalman filter (EKF), significantly improved flight test performance across the entire testing area, with a cumulative deviation of only 10.22 m, about 0.85% of the total flight distance. These results demonstrate that the proposed method not only meets accuracy requirements for position and velocity in integrated navigation but also significantly enhances multi-UAV navigation precision, particularly in scenarios with global positioning system (GPS) interference.

Adaptive tracking control of underactuated AUV with historical navigation information and piecewise weighted fractional order integration

In this study, a piecewise adaptive weighted fractional order integration (PAWFOI) is introduced in cascade-backstepping control, which improves the effectiveness of autonomous underwater vehicles (AUVs) in suppressing navigation errors during long-duration operations. Additionally, PAWFOI significantly reduces the computational complexity compared to conventional fractional order integration, ensuring a consistent computational speed. The adaptive weight selector dynamically adjusts the weights based on the AUV state information, achieving enhanced tracking performance. Based on Lyapunov’s theory, the results demonstrated that AUV tracking control can be achieved by selecting appropriate control parameters. Finally, an example is presented to verify the correctness and validity of the theoretical results.

A Visual Navigation Algorithm for UAV Based on Visual-Geography Optimization

The estimation of Unmanned Aerial Vehicle (UAV) poses using visual information is essential in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a UAV visual navigation algorithm based on visual-geography Bundle Adjustment (BA) to address the challenge of missing geolocation information in monocular visual navigation. This algorithm presents an effective approach to UAV navigation and positioning. Initially, Visual Odometry (VO) was employed for tracking the UAV’s motion and extracting keyframes. Subsequently, a geolocation method based on heterogeneous image matching was utilized to calculate the geographic pose of the UAV. Additionally, we introduce a tightly coupled information fusion method based on visual-geography optimization, which provides a geographic initializer and enables real-time estimation of the UAV’s geographical pose. Finally, the algorithm dynamically adjusts the weight of geographic information to improve optimization accuracy. The proposed method is extensively evaluated in both simulated and real-world environments, and the results demonstrate that our proposed approach can accurately and in real-time estimate the geographic pose of the UAV in a GNSS-denied environment. Specifically, our proposed approach achieves a root-mean-square error (RMSE) and mean positioning accuracy of less than 13 m.

Evidence-based Prostate Cancer Screening Interventions for Black Men: A Systematic Review.

The databases PubMed, Ovid MEDLINE, CINAHL Plus, and PsychInfo were utilized to examine peer-reviewed publications between 2017 and 2023. Data extracted included implementation plans, outcome measures, intervention details, and results of the study. The Critical Appraisal Skills Programme was used to assess the quality of the evidence presented. Of the 206 full-text articles assessed, three were included in this review. Educational interventions about prostate cancer knowledge with shared and informed decision-making (IDM) features, as well as counseling, treatment options, and healthcare navigation information, may increase prostate cancer screening participation among Black men. Additionally, health partner educational interventions may not improve IDM related to screening participation. The quality of the evidence presented in each article was valid and potentially impactful to the community. Black men face various social determinants of health barriers related to racism, discrimination, cost of health services, time away from work, and lack of trust in the healthcare system when making health-related decisions, including prostate cancer screening participation. A multifactorial intervention approach is required to address these inequities faced by Black men especially as prostate cancer is curable when diagnosed at an earlier stage.

Autonomous UAV Safety Oriented Situation Monitoring and Evaluation System

In this paper, a LabVIEW-based online monitoring and safety evaluation system for UAVs is designed to address the deficiencies in UAV flight state parameter monitoring and safety evaluation. The system consists of a lower unit for UAV recording and an upper unit on the ground. The lower unit collects and detects flight data and connects to the upper unit through a wireless digital transmission module via a serial port. The upper unit receives the data and carries out the monitoring and safety situation evaluation of the UAV. The lower unit of the system adopts multi-sensors to collect UAV navigation information in real time to achieve flight detection, while the upper unit adopts LabVIEW to design the UAV online monitoring and safety situation prediction system, enabling monitoring and safety situation prediction during UAV navigation. The test results show that the system can detect and comprehensively display the navigation information of the UAV in real time, and realize the safety evaluation and warning function of the UAV.

A scene with an invisible wall - navigational experience shapes visual scene representation.

Human navigation heavily relies on visual information. Although many previous studies have investigated how navigational information is inferred from visual features of scenes, little is understood about the impact of navigational experience on visual scene representation. In this study, we examined how navigational experience influences both the behavioral and neural responses to a visual scene. During training, participants navigated in the virtual reality (VR) environments which we manipulated navigational experience while holding the visual properties of scenes constant. Half of the environments allowed free navigation (navigable), while the other half featured an 'invisible wall' preventing the participants to continue forward even though the scene was visually navigable (non-navigable). During testing, participants viewed scene images from the VR environment while completing either a behavioral perceptual identification task (Experimentl) or an fMRI scan (Experiment2). Behaviorally, we found that participants judged a scene pair to be significantly more visually different if their prior navigational experience varied, even after accounting for visual similarities between the scene pairs. Neurally, multi-voxel pattern of the parahippocampal place area (PPA) distinguished visual scenes based on prior navigational experience alone. These results suggest that the human visual scene cortex represents information about navigability obtained through prior experience, beyond those computable from the visual properties of the scene. Taken together, these results suggest that scene representation is modulated by prior navigational experience to help us construct a functionally meaningful visual environment.

Analysis of the peculiarities of the method of combining an electronic cartographic navigation information system with a ship's radar station

The article analyses the importance of radar image overlay on the screen of an electronic charting navigation information system (ECNIS) for improving safety and efficiency of navigation. Based on a review of scientific publications and professional literature, the article discusses the technical aspects of the overlay function, its advantages and limitations, and the impact on the decision-making process of a navigator. The overlay of radar information on an electronic navigation chart ensures the integration of planned and actual data on the environment, which contributes to the increase of situational awareness of the navigator. The combination of the systems makes it possible to visually compare radar targets with cartographic objects, clarify the vessel's location, control drift and orientation, and detect navigation hazards. The overlay function is especially valuable when sailing in confined waters and in conditions of limited visibility. At the same time, the use of overlay technology is associated with a number of technical difficulties and limitations related to ensuring the accuracy of overlap, harmonisation of data formats, and synchronisation of information updates. Errors in the operation of positioning systems, direction guidance, and cartography errors can lead to inconsistencies in image layers and incorrect interpretation of the navigation situation. The issues of equipment reliability, display parameters settings, competence and training of navigators are of critical importance. It is emphasised that radar image overlay on ECDIS is an effective tool for improving safety of navigation, but it does not replace traditional navigation practices and does not eliminate the need for critical evaluation of data. Over-reliance on technology, ignoring its limitations and disregard for the principles of good maritime practice can lead to erroneous decisions and have a negative impact on safety. Further prospects for the development of technology can be associated with the improvement of information processing algorithms, optimisation of human-machine interaction, and the development of training methods. The article emphasises the need for an integrated approach combining the improvement of technical systems and the human factor to fully realise the potential of the overlay function in ensuring safety of navigation. Only a balanced use of technological innovations and traditional navigational practices can guarantee the reliability and efficiency of modern navigation

Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation information is proposed to enhance the positioning accuracy and robustness of UAV navigation systems. First, the fundamental principles of Kalman filtering and its application in navigation are introduced. Second, the basic principles of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and their applications in the navigation domain are elaborated. Subsequently, an algorithm based on a CNN and LSTM-assisted Kalman filtering fusion navigation is proposed. Finally, the feasibility and effectiveness of the proposed algorithm are validated through experiments. Experimental results demonstrate that the Kalman filtering fusion navigation algorithm assisted by a CNN and LSTM significantly improves the positioning accuracy and robustness of UAV navigation systems in highly interfered complex environments.

DETECTION OF LPG VEHICLES IN RISKY AREAS WITH COMPUTER VISION TECHNIQUES

Vehicle use is becoming more widespread day by day due to the world population growth. Within the scope of intelligent transportation systems, the information technologies sector and the transportation sector work in an integrated manner to solve the problems caused by the increasing number of vehicles. Data obtained with sensors and cameras are analyzed with artificial intelligence-based information technologies and used in autonomous vehicles, security, traffic management, navigation and passenger information systems. Computer vision enables machines to extract meaningful patterns and relationships from images by combining image processing and deep learning technologies. Computer vision techniques are applied in many fields such as tourism, health, industry, defense, transportation, service, e-commerce, etc. The applications developed provide solutions to various challenges in the transportation sector. For vehicles using Liquified Petroleum Gas (LPG) fuel, the gases in LPG tanks are flammable and pose a potential explosion hazard, especially in certain areas in cities. Entry of LPG vehicles is prohibited in institutions and organizations such as hospitals, shopping malls, hotels that have indoor parking services. The control method of the ban is carried out by assigning a personnel and checking the vehicle trunks. In this study, LPG fueled vehicles were automatically detected using computer vision techniques. Vehicle image data captured by mobile cameras in different provinces in Turkey were trained and compared with four different deep learning models. As a result of training and performance tests on the models, the YOLOv8 model was more effective than the other models with an accuracy of 0.994 mAP and a speed of 11.6 ms. It has been shown to be a stable model in terms of real-time monitoring in real life. It is envisaged that the developed system can contribute to the applications of computer vision techniques as well as benefit the national economy, public life safety and environmental protection.

Soft-tissue sound-speed-aware ultrasound-CT registration method for computer-assisted orthopedic surgery.

Ultrasound (US) has been introduced to computer-assisted orthopedic surgery for bone registration owing to its advantages of nonionizing radiation, low cost, and noninvasiveness. However, the registration accuracy is limited by US image distortion caused by variations in the acoustic properties of soft tissues. This paper proposes a soft-tissue sound-speed-aware registration method to overcome the above challenge. First, the feature enhancement strategy of multi-channel overlay is proposed for U2-net to improve bone segmentation performance. Secondly, the sound speed of soft tissue is estimated by simulating the bone surface distance map for the update of US-derived points. Finally, an iterative registration strategy is adopted to optimize the registration result. A phantom experiment was conducted using different registration methods for the femur and tibia/fibula. The fiducial registration error (femur, 0.98 ± 0.08mm (mean ± SD); tibia/fibula, 1.29 ± 0.19mm) and the target registration error (less than 2.11mm) showed the high accuracy of the proposed method. The experimental results suggest that the proposed method can be integrated into navigation systems that provide surgeons with accurate 3D navigation information.

Estimation of near-coastal bathymetry using AIS ship movements

In near coastal environments, nautical charts provide crucial information for navigation and routing both in real-time operations and during planning stages. The cost of data collection as well as capacity constraints in the processing pipeline make reliable bathymetric information in such areas sparse. Prioritization rules can help guide the efforts to where information is the most valuable. AIS data provide accounts of real ship movements, indicating both desirable paths and minimum depths. We propose a statistical model for combining sparse bathymetric soundings with AIS observations for improved prediction of depths for generation of feasible transportation corridors. The method relies on viewing AIS draughts as censored observations of the true depth. A case-study is performed for the southern archipelago of Gothenburg using the program R-INLA. The non-stationarity caused by having boundaries with known (zero) depth and holes (land) in the domain is handled through discretization. Varying amounts of AIS data, ranging from none to 1824 observations, are used in the experiments. Results show predicted depths within the range of data values, and that inclusion of AIS data serve to push the field down to ensure that traverseable areas are predicted as such revealing corridors in narrow passages where bathymetric soundings are lacking.

Indoor augmented reality (AR) pedestrian navigation for emergency evacuation based on BIM and GIS

Nowadays with the increase of high-rise buildings, emergency evacuation is an indispensable part of urban environment management. Due to various disaster incidents occurred in indoor environments, research has concentrated on ways to deal with the different difficulties of indoor emergency evacuation. Although global navigation satellite systems (GNSSs) such as global positioning system (GPS) come in handy in outdoor spaces, they are not of much use in enclosed places, where satellite signals cannot penetrate easily. Therefore, other approaches must be considered for pedestrian navigation to cope with the indoor positioning problem. Another problem in such environments is the information of the building indoor space. The majority of the studies has used prepared maps of the building, which limits their methodology to that specific study area. However, in this study we have proposed an end-to-end method that takes advantage of BIM model of the building, thereby applicable to every structure that has an equivalent building information model (BIM). Moreover, we have used a mixture of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) method with relatively higher accuracy compared to other similar methods for navigating the user to the exit point. For implementing PDR, we used the sensors in smartphones to calculate user steps and direction. In addition, the navigational information was superimposed on the smartphone screen using augmented reality (AR) technology, thus communicating the direction information in a user-friendly manner. Finally, the AR mobile emergency evacuation application developed was assessed with a sample audience. After an experience with the app, they filled out a questionnaire which was designed in the system usability scale test (SUS) format. The evaluation results showed that the app achieved an acceptable suitability for usage.

Brain-Inspired Multisensor Navigation Information Fusion Model Based on Spatial Representation Cells

Brain-Inspired Multisensor Navigation Information Fusion Model Based on Spatial Representation Cells

Kalman filter for radio source power and direction of arrival estimation

Images are an important source of information for spacecraft navigation. Based on an image and a known attitude, triangulation techniques (intersection or resection) are often used for positioning and navigation. In the resection problem, an observer estimate its unknown location by using angle measurements to points at known locations (i.e., landmarks), the localization performance depending on the accuracy of the angle measurements. As a contribution to resection for spacecraft navigation, we considers the dynamic image estimation problem based on radio interferometry, i.e., image of radio source power, where the measurements are sample covariance matrices (SCMs). Considering the case where several measurements are available as well as a known dynamic linear model of image evolution, a.k.a a linear state model, the minimum mean-squared error image estimator (MMSE) is given by the Kalman filter (KF) or one of its variants. However standard Kalman-like filters are not a priori suitable for the problem at hand since the measurements (i.e., SCMs) cannot be formulated analytically as a function of state parameters to be estimated (i.e., radio source power). In fact, this lack of analytical formulation can be circumvented by a statistical linear fitting allowing the SCMs to be expressed in terms of the state. This linear fitting introduces an additive residual noise, equivalent to a measurement noise, whose covariance matrix depends on the current state, a non-standard case for a measurement model. The covariance matrix of the residual noise is derived whatever the distributions of the radio sources and of the additive noise at the samples level, unveiling the contribution of their multivariate kurtosis. The proposed method is evaluated on simulated data representative of a dynamic radio interferometry framework. The results show that the proposed method is capable of effectively tracking moving radio sources in complex scenes with theoretical guaranties when the signal multivariate kurtosis is known.

Augmented sensing-based on-orbit configuration estimation for large-scale distributed spacecraft systems

On-orbit configuration estimation is one of the most important tasks for large-scale distributed spacecraft systems, yet incomplete measurement topologies limit the acquisition of partial inter-satellite relative navigation information. In this article, an on-orbit configuration estimation algorithm based on augmented sensing is proposed, which enables each spacecraft to extend the estimation range beyond the observation range without any communication overhead. Referring to the Bayesian inference process, the cooperative behavior of the large-scale distributed spacecraft system is applied to infer relative motion states of unobservable spacecrafts. Under the framework of scaled unscented transformations (SUT), we achieve high-accuracy configuration estimation for the large-scale distributed spacecraft system. Finally, the proposed algorithm is verified in a high-orbiting gravitational wave detector constellation with inter-satellite laser interferometers.

A dynamic topology analysis method for multi-ship encounters based on multi time-space network trees

This paper innovatively proposes a novel approach of topological analysis in the dynamic time-space domain of ship encounters under the joint action of multi-space network structural elements, based on the GIS spatial information platform and spatial topology analysis theory. The proposed method based on the ship's navigational motion information, encounter dynamic mode, relative time-space position distribution, etc., the method divides and intersects the time-space domain of the ship's encounter dynamics, and establishes an encountering dynamic time-space network tree in which space and attribute elements are collaboratively associated during the navigation. From the perspective of network topology analysis, the high-precision analysis of the spatial coupling characteristics and time-space trends of potential encountered dangers during ships dynamic navigation is achieved. To verify the effectiveness of the method, real encounter scenarios of M/T SANCHI and M/V CF Crystal were used as analysis cases. The results showed that the proposed method can precisely extract spatial relationships and spatio-temporal coupling feature information of potential hazards encountered between ships, with an average error of about 2.54% and an overall error between 1.94% and 3.53%.Simultaneously, the method's time efficiency results are thoroughly studied and compared, and it is discovered that the proposed method has good stability.

A tightly coupled GNSS RTK/IMU integration with GA-BP neural network for challenging urban navigation

Abstract Intelligent transportation system is increasing the importance of real-time acquisition of positioning, navigation, and timing information from high-accuracy global navigation satellite systems (GNSS) based on carrier phase observations. The complexity of urban environments, however, means that GNSS signals are prone to reflection, diffraction and blockage by tall buildings, causing a degraded positioning accuracy. To address this issue, we have proposed a tightly coupled single-frequency multi-system single-epoch real-time kinematic (RTK) GNSS/inertial measurement unit (IMU) integration algorithm with the assistance of genetic algorithm back propagation based on low-cost IMU equipment for challenging urban navigation. Unlike the existing methods, which only use IMU corrections predicted by machine learning as a direct replacement of filtering corrections during GNSS outages, this algorithm introduces a more accurate and efficient IMU corrections prediction model, and it is underpinned by a dual-check GNSS assessment where the weights of GNSS measurements and neural network predictions are adaptively adjusted based on duration of the integrated system GNSS failure, assisting RTK/IMU integration in GNSS outages or malfunction conditions. Field tests demonstrate that the proposed prediction model results in a 68.69% and 69.03% improvement in the root mean square error in the 2D and 3D component when the training and testing data are collected under 150 s GNSS signal-blocked conditions. This corresponds to 52.43% and 51.27% for GNSS signals discontinuously blocked with 500 s.

Optimal path planning for a ship in coastal waters with deep Q network

With the development of autonomous ships, complete automation of passage planning has become an imminent priority. However, existing A* algorithms have the disadvantage of generating paths close to land because they prioritize minimizing the navigation distance. Therefore, this study proposes a method based on a deep Q network to effectively implement reward-and-penalty strategies considering required navigation areas and non-navigable areas throughout the port-to-port distance. First, the Busan and Gwangyang Ports were selected as the target areas and a container ship was selected as the target ship. Initially, non-navigable and reward areas were designated based on the water depth and electronic navigational chart information. Thereafter, we conducted experiments using algorithms in three types of environments: normal conditions, turbulent weather, and obstacle-involved environments. Furthermore, we employed the Douglas–Peucker algorithm to eliminate excessive waypoints. Experimental results demonstrated that the path planning of a ship obtained using a deep Q network involved more efficient and safer decisions for ship navigation. Furthermore, the navigation distance was reduced by 1.77% compared to the passage plan used by actual ships. The proposed approach is advantageous for automatically deriving the optimal mid-range path of ships and can thus contribute toward improving maritime safety and efficiency.