Global Positioning System Navigation Research Articles

GPS/BDS triple-frequency cycle slip detection and repair based on moving window global search method

Cycle slip detection and repair are crucial steps in achieving high accuracy in Global Navigation Satellite System (GNSS) data processing. The use of Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) triple frequency observations allows for more accurate detection and repair of cycle slips compared to single or dual frequency. This study presents a moving window global search method by selecting three sets of combined coefficients to construct geometry-free (GF) models to minimize the influence of the ionosphere, using a moving window to update the standard deviation of cycle slip estimation, applying the "3 σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upsigma$$\\end{document}" criterion to constrain the range, and utilizing a global search method to detect and repair triple-frequency cycle slips. Through five sets of 1 Hz GNSS data experiments, the results demonstrate the effectiveness of this method in determining the position and size of triple-frequency cycle slips while avoiding multi-value problems. The detection success rate for GPS ranges from 98.0 to 100.0%, while BDS ranges from 92.0 to 100.0%. On average, GPS achieves a detection rate of 99.2%, and BDS reaches 96.7%, which is 0.8% and 1.8% higher than the direct rounding method, respectively. Compared to existing methods, it is also effective for the vast majority of small cycle slips within 2 cycles.

Retrieval of sea ice thickness from FY-3E data using Random Forest method

In this study, we employ a Random Forest approach to estimate sea ice thickness (SIT) using Fengyun-3E (FY-3E) and Soil Moisture Ocean Salinity (SMOS) data. This method relies on four input parameters: incidence angle (θ), reflectivity (Γ), sea ice salinity (S), and sea ice temperature (T). In addition, FY-3E can receive both Global Positioning System (GPS) and Beidou Navigation Satellite System (BDS) reflected signals. Evaluation for the Arctic region based on data spanning from October 2022 to April 2023 reveals that the proposed models trained on GPS and BDS signals from FY-3E achieve high consistency and low error. Take GPS signals as an example, coefficients of determination are 0.97 and 0.91 and mean absolute errors are 0.019 m and 0.032 m for the training and test sets, respectively. In general, SIT inversion based on GPS signals slightly exhibits a higher accuracy than that based on BDS signals, but both approaches display high performances. The areas with the highest accuracy of SIT estimation based on GPS and BDS signals are the Shelikhov Bay and the Okhotsk Sea, followed by the Bering Sea and the Bering Strait. We conclude that machine learning and data fusion are effective for SIT estimation.

Real-Time Precise Orbit Determination of Low Earth Orbit Satellites Based on GPS and BDS-3 PPP B2b Service

This study investigates and verifies the feasibility of the precise point positioning (PPP)-B2b enhanced real-time (RT) precise orbit determination (POD) of low Earth orbit (LEO) satellites. The principles and characteristics of matching various PPP-B2b corrections are introduced and analyzed. The performance and accuracy of broadcast ephemeris and PPP-B2b signals are compared and evaluated by referring to the precise ephemeris. The root mean square (RMS) errors in the Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS)-3 broadcast ephemeris orbits in the along direction are larger than those in the other two (radial and cross) directions, and correspondingly, the along component PPP-B2b corrections are greatest. The continuity and smoothness of the GPS and BDS-3 broadcast ephemeris orbits and clock offsets are improved with the PPP-B2b corrections. The availability of PPP-B2b corrections is comprehensively analyzed for the TJU-01 satellite. Several comparative schemes are adopted for the RT POD of the TJU-01 satellite using the broadcast ephemeris and PPP-B2b corrections. The RT POD performance is improved considerably with the broadcast ephemeris corrected by the PPP-B2b signals. The RMS of the RT orbital errors in the radial, along, and cross directions is 0.10, 0.13, and 0.09 m, respectively, using BDS-3 and GPS PPP-B2b corrections, with reference to the solutions calculated with the precise ephemeris. The accuracy is improved by 5.1%, 43.9%, and 28.7% in the three directions, respectively, relative to that achieved with the broadcast ephemeris. It is concluded that a greater proportion of received PPP-B2b satellite signals corresponds to a greater improvement in the accuracy of the RT POD of the LEO satellite.

Unmanned Aerial Vehicle Based Power Pole Localization Using Image Stitching

When facing natural disasters, power transmission and distribution towers are prone to tilting, collapsing, and other situations, which can easily lead to line faults and subsequently cause power outage incidents. In order to quickly and accurately locate abnormal towers and restore power supply promptly, the power maintenance department typically employs unmanned aerial vehicles (UAVs) for post-disaster inspections of power line towers. Traditional methods for UAV ground target localization mainly rely on the UAV’s own position, attitude, and onboard sensor information to indirectly calculate the target’s position. This method is influenced by the Global Positioning System (GPS) and Inertial Navigation System (INS), relying on high-precision sensors. To achieve precise positioning of power line towers with simplified equipment on UAVs, this paper proposes a visual passive localization method based on ground control points. Addressing the sparse distribution of ground control points, a solution based on image stitching is introduced to overcome difficulties in localization caused by insufficient ground control points.

A Robust GPS Navigation Filter Based on Maximum Correntropy Criterion with Adaptive Kernel Bandwidth

Multiple forms of interference and noise that impact the receiver’s capacity to receive and interpret satellite signals, and consequently the preciseness of positioning and navigation, may be present during the processing of Global Positioning System (GPS) navigation. The non-Gaussian noise predominates in the signal owing to the fluctuating character of both natural and artificial electromagnetic interference, and the algorithm based on the minimum mean-square error (MMSE) criterion performs well when assuming Gaussian noise, but drops when assuming non-Gaussian noise. The maximum correntropy criteria (MCC) adaptive filtering technique efficiently reduces pulse noise and has adequate performance in heavy-tailed noise, which addresses the issue of filter performance caused by the presence of non-Gaussian or heavy-tailed unusual noise values in the localizing measurement noise. The adaptive kernel bandwidth (AKB) technique employed in this paper applies the calculated adaptive variables to generate the kernel function matrix, in which the adaptive factor can modify the size of the kernel width across a reasonably appropriate spectrum, substituting the fixed kernel width for the conventional MCC to enhance the performance. The conventional maximum correntropy criterion-based extended Kalman filter (MCCEKF) algorithm’s performance is significantly impacted by the value of the kernel width, and there are certain predetermined conditions in the selection based on experience. The MCCEKF with a fixed adaptive kernel bandwidth (MCCEKF-AKB) has several advantages due to its novel concept and computational simplicity, and gives a qualitative solution for the study of random structures for generalized noise. Additionally, it can effectively achieve the robust state estimation of outliers with anomalous values while guaranteeing the accuracy of the filtering.

Alternative Positioning, Navigation, and Timing Enabled by Games in Satisfaction Form and Reconfigurable Intelligent Surfaces

The potential degradation of the Global Positioning System and other Global Navigation Satellite Systems under several circumstances gives rise to the development of alternative position, navigation, and timing (PNT) technologies aiming at maintaining efficient and safe operations. In this article, we exploit the use of reconfigurable intelligent surfaces (RISs) as enablers for the design of an alternative PNT solution, with improved accuracy and efficiency. The specific problem of RISs' orchestration and configuration is treated via the adoption of game theory and reinforcement learning (RL). Initially, a satisfaction game is formulated and solved among the targets, enabling them to autonomously determine the optimal number of RISs that will contribute to their PNT service, while the specific set of RISs to be used is determined by a novel RL algorithm. In order to further maximize the received signal strength at each target of the reflected signals from the specific set of RISs, the phase-shift optimization of the latter is performed. Based on the above, an iterative least squares algorithm is adopted, following the multilateration technique, in order for each target to estimate its position and timing. The performance evaluation of the proposed approach is achieved via modeling and simulation.

Inconsistent pseudorange biases in time links and their effect on BDS and GPS PPP frequency transfer

Precise point positioning (PPP) using the Global Navigation Satellite System (GNSS) phase and code measurements has become the primary technique for time and frequency comparisons. Several scholars have studied multi-GNSS PPP clock comparisons; however, the inconsistent pseudorange bias from receivers with different correlator spaces and front-end designs in pseudorange observations has not been considered. In this study, we analyze the characteristics of the inconsistent biases of the Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) and their effects on PPP frequency transfer. The biases are confirmed to exist in receivers from Septentrio, Trimble, Leica and differ by manufacturer. To explicitly investigate the pseudorange bias effects of GPS and BDS on the PPP time and frequency comparisons, nine stations with receivers from three different manufacturers that could track the BDS-2 and BDS-3 signals were selected. Regarding PPP frequency transfer with inhomogeneous receivers, the Modified Allan Deviations (MDEVs) of the GPS and BDS PPP frequency stabilities were significantly optimized after using bias corrections. According to the receiver type classification strategy, the overall improvements in frequency transfer with Trimble-Septentrio and Trimble-Leica can be reached up to 31%, 29%, and 39%, and 38%, 29%, and 31% for GPS, BDS-2, and BDS-3, respectively. Moreover, the convergence time of the clock comparisons was shortened after bias corrections were applied. For GOPE-BRUX and GOPE-MATE links, the corrected cases yielded average clock difference stability improvements of 38%, 35%, and 53% and 35%, 35%, and 23% for GPS, BDS-2, and BDS-3, respectively. The results show that bias corrections are vital and allow for more stable time links for PPP frequency transfer.

Radar sensor based machine learning approach for precise vehicle position estimation

Estimating vehicles’ position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles’ position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS), pose significant data delays and data transmission errors, which render them ineffective in achieving precision in vehicles’ position estimation, especially under dynamic environments. Moreover, the existing radar-based approaches proposed for position estimation utilize the static values of range and azimuth, which make them inefficient in highly dynamic environments. In this paper, we propose a radar-based relative vehicle positioning estimation method. In the proposed method, the dynamic range and azimuth of a Frequency Modulated Continuous Wave radar is utilized to precisely estimate a vehicle’s position. In the position estimation process, the speed of the vehicle equipped with the radar sensor, called the reference vehicle, is considered such that a change in the vehicle’s speed changes the range and azimuth of the radar sensor. For relative position estimation, the distance and relative speed between the reference vehicle and a nearby vehicle are used. To this end, only those vehicles are considered that have a higher possibility of coming in contact with the reference vehicle. The data recorded by the radar sensor is subsequently utilized to calculate the precision and intersection Over Union (IOU) values. You Only Look Once (YOLO) version 4 is utilized to calculate precision and IOU values from the data captured using the radar sensor. The performance is evaluated under various real-time traffic scenarios in a MATLAB-based simulator. Results show that our proposed method achieves 80.0% precision in position estimation and obtains an IOU value up to 87.14%, thereby outperforming the state-of-the-art.

An evaluation of GNSS radio occultation atmospheric profiles from Sentinel-6

The Global Navigation Satellite System (GNSS) radio occultation (RO) significantly impacts climate change monitoring. GNSS RO is based mainly on the refraction of the GNSS signals transmitted by satellites and received by a receiver on a Low Earth Orbit (LEO) satellite. Sentinel-6 (S6) is the first satellite from the Sentinel missions to retrieve atmospheric parameters using the RO technique. Temperature, pressure, and water vapor profiles can be retrieved using S6 based on the refraction of GNSS signals. This research evaluated the distribution and coverage of GNSS RO data from S6. This research also aims to evaluate the results of GNSS RO products, especially temperature from S6, from January 2022 to June 2022. The results of S6 are compared with the results from the Meteorological Operational Polar Satellite series (MetOp-B and MetOp-C) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset. Our results indicate that S6 allows for many RO observations in space by receiving signals from the Global Positioning System (GPS) and Russia's Global Navigation Satellite System (GLONASS). S6 provides monthly RO profiles from GPS that are greater than monthly RO profiles from GLONASS. Results show that S6 provides more GNSS RO data than MetOp-B and MetOp-C. The findings indicate that the distribution of GNSS RO events from S6 over the globe is uniform in longitude and non-uniform at latitude, with the minimum number at the poles. This paper makes a significant contribution to exploring the atmosphere with the S6 as a promising satellite for weather forecasting and monitoring climate change.

With life there is motion. Activity biomarkers signal important health and performance outcomes.

Measures of human motion provide a rich source of health and physiological status information. This paper provides examples of motion-based biomarkers in the form of patterns of movement, quantified physical activity, and characteristic gaits that can now be assessed with practical measurement technologies and rapidly evolving physiological models and algorithms, with research advances fed by the increasing access to motion data and associated contextual information. Quantification of physical activity has progressed from step counts to good estimates of energy expenditure, useful to weight management and to activity-based health outcomes. Activity types and intensity durations are important to health outcomes and can be accurately classified even from carried smart phone data. Specific gaits may predict injury risk, including some re-trainable injurious running or modifiable load carriage gaits. Mood status is reflected in specific types of human movement, with slumped posture and shuffling gait signaling depression. Increased variability in body sway combined with contextual information may signify heat strain, physical fatigue associated with heavy load carriage, or specific neuropsychological conditions. Movement disorders might be identified earlier and chronic diseases such as Parkinson's can be better medically managed with automatically quantified information from wearable systems. Increased path tortuosity suggests head injury and dementia. Rapidly emerging wear-and-forget systems involving global positioning system and inertial navigation, triaxial accelerometry, smart shoes, and functional fiber-based clothing are making it easier to make important health and performance outcome associations, and further refine predictive models and algorithms that will improve quality of life, protect health, and enhance performance.

Cosmic coding and transfer storage (COSMOCATS) for invincible key storage

Thus far, a perfectly secure encryption key storage system doesn’t exist. As long as key storage is connected to a network system, there is always a chance that it can be cracked. Even if storage is not continually connected to a network system; it is repeatedly necessary for an individual to access storage to upload and download the data; hence there is always a loophole with every conventional encryption key storage system. By utilizing the penetrative nature of cosmic-ray muons, the COSMOCAT (Cosmic coding and transfer) technique may tackle this problem by eliminating the requirement for any network connection to data storage. COSMOCAT was invented as a post quantum key generation and distribution technique for wireless near field communication. However, in its first stage of development, COSMOCAT relied on standard comparators and Global Positioning System (GPS) or other Global Navigation Satellite Systems (GNSS) for key generation. Temporal jitters of the signals outputted from comparators and frequency fluctuations in GPS-disciplined oscillators degraded the key strength and the efficiency of both the key generation and distribution. New strategies are tested in this paper to improve these factors. As a result, the key strength and the key authenticating rate limit are respectively improved by 4 orders of magnitude and more than 5 orders of magnitude. As a consequence, it has become possible to propose a practical methodology for a new key storage and authentication strategy which has the potential to be an impregnable defense against any kind of cyber/physical attack to data storage. Practical applications of COSMOCATS-based symmetric-key cryptosystems to an electronic digital signing system, communication, and cloud storage are also discussed.

GNSS rapid precise point positioning enhanced by low Earth orbit satellites

The Low Earth Orbit (LEO) satellites can be used to effectively speed up Precise Point Positioning (PPP) convergence. In this study, 180 LEO satellites with a global distribution are simulated to evaluate their contribution to the PPP convergence. LEO satellites can give more redundant observations and improve satellite geometric distributions, particularly for a single Global Navigation Satellite System (GNSS). The convergence speed of the PPP float solution using the Global Positioning System (GPS, G) or BeiDou Navigation Satellite System (BDS, C) single system as well as the G/C/Galileo navigation satellite system (Galileo, E)/GLObal NAvigation Satellite System (GLONASS, R) combined system with LEO satellites added is improved by 90.0%, 91.0%, and 90.7%, respectively, with respect to the system without LEO satellites added. We introduced LEO observations to assist GNSS in PPP-AR (Ambiguity Resolution) and PPP-RTK (Real Time Kinematic). The success fix rate of a single system is significantly improved, and the Time-To-First-Fix (TTFF) of G and G/C/E is reduced by 86.4% and 82.8%, respectively, for the PPP-AR solution. We analyzed the positioning performance of LEO satellite assisted G/C/E PPP-RTK in the reference networks of different scales, namely different atmospheric delay interpolation accuracies. The success fix rate of the G/C/E combined system is improved from 86.8 to 94.9%, and the TTFF is reduced by 36.8%, with the addition of LEO satellites in the 57 km reference network. In the 110 km reference network, the success fix rate of the G/C/E combined system is improved from 64.0 to 88.6%, and the TTFF is reduced by 32.1%. GNSS PPP-RTK with adding the LEO satellites in the reference networks of different scales shows obvious improvement because the atmospheric correlation decreases with increasing distance from the reference networks.

Optimal GPS Acquisition Algorithm in Severe Ionospheric Scintillation Scene

The Global Positioning System (GPS) plays an important role in navigation and positioning services. When GPS signals propagate through a complex space environment, they are susceptible to interference of ionospheric scintillation. As one of the biggest interference sources on GPS navigation and positioning, ionospheric scintillation will lead to signal intensity decline and carrier phase fluctuation, making signal acquisition of the GPS receiver challenging. Thus, an acquisition algorithm based on differential coherent integration combining accumulation correlation and bit sign transition estimation is proposed. The coherent accumulation is applied to reduce computational loads and contribution by the Gaussian white noise in the signal. Moreover, the differential coherence integration is utilized to eliminate data blocks with bit transition, prolonging the coherence integration time and improving the data utilization rate. Experimental results show that under severe ionospheric scintillation condition, weak GPS signals can be acquired successfully after improving the acquisition algorithm, with the acquisition probability reaching 50% when the signal-to-interference ratio (SIR) drops to −34 dB. Comparing to the differential coherence integration, the complexity of the calculation reduces to only 21.75% effectively after the improvement. The execution time is less than half of the differential coherence integral.

Interacting Multiple Model Filter with a Maximum Correntropy Criterion for GPS Navigation Processing

In order to deal with the uncertainty of measurement noise, particularly for outlier types of multipath interference and non-line of sight (NLOS) reception, this paper proposes a novel method for processing the navigation states of the Global Positioning System (GPS) that combines the maximum correntropy criterion (MCC) and the interacting multiple model (IMM), with an extended Kalman Filter (EKF). Multipath mitigation is essential for increased positioning accuracy since multipath interference is one of the primary sources of errors. Nonlinear filtering with IMM configuration uses filter structural adaptation. In processing time-varying satellite signal standards for GPS navigation, it offers an alternative for creating the adaptive filter. A collection of switching models built on a method of multiple model estimation can be used to characterize the uncertainty of the noise. Even though most noise in real life is non-Gaussian, time-varying, and of fluctuating strength, the standard EKF operates effectively when the noise is Gaussian. The performance of EKF will drastically decline if the signals appear non-Gaussian. The underlying system disrupted by heavy-tailed, non-Gaussian impulsive sounds could be better since the EKF employs second-order statistical information. The MCC is a method for comparing two random variables based on higher-order signal statistics. The maximum correntropy-extended Kalman filter (MCEKF), which uses the MCC rather than the minimal mean square error (MMSE) as the optimization criterion, is used to enhance performance in non-Gaussian situations. Finally, a performance evaluation will be conducted to compare the effectiveness of the suggested strategy in improving positioning to alternative system designs.

Sensor-Based Adaptive Estimation in a Hybrid Environment Employing State Estimator Filters

It is widely acknowledged that navigation is a significant source of between sites. The Global Positioning System (GPS) has numerous navigational advancements, and hence it is used widely. GPS navigation can be compromised at any level between position, location, and estimation, to the detriment of the user. Consequently, a navigation system requires the precise location and underpinning tracking of an object without signal loss. The objective of a hybrid environment prediction system is to foresee the location of the user and their territory by employing a variety of sensors for position estimation and monitoring navigation. This article presents a state estimation of the relative position for indoor and outdoor activity solved with a state estimation algorithm utilizing Kalman filter. Also, a comparative study of variants of the Kalman filter, where linearizing current mean and covariance with nonlinear state estimation as an approach of Extended Kalman Filter (EFK) is applied to the collected data. The third comparative aspect uses probability distribution for the selected points with a Sigma Point Kalman Filter (SPKF) for evaluating an accelerometer, gyroscope, and GPS data in hybrid environments for various activities for different data collection scenarios from users. The findings of the presented model demonstrate the robust performance of all forms of the Kalman filter algorithm for diverse user-performed activities in totally contaminated indoor and outdoor environments. Experimental findings with various patterns and data, conducted by different subjects using multiple modes of navigation, show that the approach can indeed lead to the intelligent development of sensor-based navigation and monitoring. State estimation and prediction is extraordinarily beneficial for mining applications, autonomous vehicle localization/tracking, and location-based services. This research work demonstrates both EKF-based and SPKF-based sensor fusion to provide an appropriate estimation.

Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry

AbstractAlthough reflectometry is not the primary application of Global Positioning System (GPS) and similar Global Navigation Satellite Systems (GNSS), fast‐growing GNSS tracking networks has led to the emergence of GNSS interferometric reflectometry technique for monitoring surface changes such as water level. However, scientific‐grade or geodetic GNSS instruments are expensive, which is a limiting factor for their prompt and more widespread deployment as a dedicated environmental sensor. We present a prototype called Raspberry Pi Reflector (RPR) that includes a low‐cost and low‐maintenance single‐frequency GPS module and a navigation antenna connected to an inexpensive Raspberry Pi microcomputer. A unit has been successfully operating for almost 2 years since March 2020 in Wesel (Germany) next to the Rhine river. Sub‐daily and daily water levels are retrieved using spectral analysis of reflection data. The river level measurements from RPR are compared with a co‐located river gauge. We find an Root‐Mean‐Square Error (RMSE) of 7.6 cm in sub‐daily estimates and 6 cm in daily means of river level. In August 2021, we changed the antenna orientation from upright to sideways facing the river. The RMSE reduced to 3 cm (sub‐daily) and 1.5 cm (daily) with the new orientation. While satellite radar altimetry techniques have been utilized to monitor water levels with global coverage, their measurements are associated with moderate uncertainties and temporal resolution. Therefore, such low‐cost and high‐precision instruments can be paired with satellite data for calibrating, validating and modeling purposes. These instruments are financially (<US$ 150 as of November 2021) and technically accessible worldwide.

A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages

To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extreme learning machine with a dynamic forgetting factor (DOS-ELM) algorithm is used to train the mapping model between the SINS’ acceleration, specific force, speed/position increments outputs, and the GPS’ speed/position increments. When a GPS signal is unavailable, GPS speed/velocity measurements are replaced with prediction output of the well-trained DOS-ELM module’s prediction output, and information fusion with the SINS reduces the degree of system error divergence. A land vehicle field experiment’s actual sensor data were collected online, and the DOS-ELM-aided methodology for the SINS/GPS integrated navigation systems was applied. The simulation results indicate that the proposed methodology can reduce the degree of system error divergence and then obtain accurate and reliable navigation information during GPS outages.

Does global positioning system-based navigation dependency make your sense of direction poor? A psychological assessment and eye-tracking study.

BackgroundGlobal positioning system (GPS)-based navigation apps are very useful in our lives. However, whether and how the usage of these apps affects spatial cognition and the sense of direction is still unclear.MethodsA total of 108 individuals were recruited and completed the GPS dependence, internet gaming behavior, and impulsivity test using scales. The eye-tracking-based general mental rotation (MR) task and target finding (TF; require individuals to find a target specified in a 3D street map in a rotated version of top 2D view map) task were used to assess their spatial cognition and the sense of direction. The correlation was used to relate GPS navigation usage, spatial cognition ability, and impulsivity. Subgroup analyses stratifying by gaming hours of individuals (< 2 h or ≥ 2 h) or maps (countryside or city) in TF task were performed. The moderating and mediating effect analyses were conducted to verify these relationships.ResultsThe GPS dependency score was nominal positively correlated with fixations in the TF task in the entire cohort (r = 0.202, unadjusted p = 0.036); it was significant in city (r = 0.254, p = 0.008) and gaming time of < 2 h (r = 0.459, p = 0.001) subgroups. The high-score (upper 30%) group of GPS dependency had more fixations on the original target building in the training area and indicative building in the test area than the low-score (lower 30%) group. GPS dependency was not associated with the correct rate and reaction time in the TF task or any of the indicators in the MR task (p > 0.05). The GPS dependency mediated the indirect effect of impulsivity on the fixations on TF. The internet gaming time moderated the association between GPS dependency and fixations on TF.ConclusionThe dependency on GPS-based navigation apps was associated with impaired spatial cognition but may not significantly affect the sense of direction.

Indoor Positioning and Navigating System Application Using Wi-Fi with Fingerprinting Method and Weighted K-Nearest Neighbor Algorithm

The need for accurate indoor location determination, object tracking, digital maps and indoor travel routes is increasing along with the construction of buildings that have complex and spacious layouts. The current Global Positioning System navigation system is only effective for outdoor use. However, when used indoors it becomes inaccurate due to factors such as signal attenuation and multipath caused by wall obstructions in the building. This study designed an application of Indoor Positioning and Navigating System Using Wi-Fi with Fingerprinting method and Weighted K-Nearest Neighbor algorithm. In the design process, it is necessary to create a fingerprinting database by considering the number of Access points and environmental conditions. Based on the results of the study, the location results of the application show that from floors 1,2, and 3. Floor 1 has a room accuracy result of 89% and a point accuracy of 86% with an average deviation of 1.42 px or 0.9 m, floor 2 has room accuracy results. of 65% and a point accuracy of 70% with an average deviation of 2.43 px or 1.7 m, and the 3rd floor has a room accuracy of 86% and a point accuracy of 68% with an average deviation of 2.27 or 1.5 m. Based on the data above, this application is proven to be able to detect the position of someone in the room with a success percentage on the 1st floor by 90%, the 2nd floor by 55%, and the 3rd floor by 80%.

SAVE ME EMERGENCY DETECTION IN MOBILE APPLICATION FOR TEENAGERS

The smartphone comes with many advanced features such as Wi-Fi, Global Positioning System (GPS) navigation, high-resolution cameras, and a touchscreen with broadband access that help users stay connected worldwide. This study focused on the development of a system framework for mobile application in emergency services that triggered fall detection. The mobile application is named Save Me! Application (app), where it can provide a simple medium to provide safety awareness to public people, especially among teenagers. The app can pinpoint the victim’s exact location when the device triggers fall detection. Subsequently, the message can be distributed to relatives, parents, or friends who have been pre-registered in the app to help the victim. The app also offers additional protection to parents or families who are busy working so that they can keep track of their children.