Real-time Positioning System Research Articles

A real-time accurate positioning system using BLE and wireless mesh network in a shipyard environment

Digitalization of the shipyard environment is a challenging problem, and also it is necessary for competing the international ship manufacturers. Thus, this study introduced a real-time accurate positioning system that is an indispensable part of a digital manufacturing system. The system implementation and measurements took place in Sedef Shipyard, the largest shipbuilding facility in Turkey. Since the shipyard includes indoor and outdoor environments, Bluetooth Low Energy (BLE) systems provide the best solution for locating the staff. The most challenging problem is to determine the positions in the metallic surroundings. The constructed system solves this problem by placing gateways and sensors at essential locations and using a mesh network. With the designed user interface, the position of the staff can be monitored accurately in real time, and reports can be generated.

A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns.

The successful emergence of real-time positioning systems in the maritime domain has favored the development of data infrastructures that provide valuable monitoring and decision-aided systems. However, there is still a need for the development of data mining approaches oriented to the detection of specific patterns such as unusual ship behaviors and collision risks. This research introduces a CSBP (complex ship behavioral pattern) mining model aiming at the detection of ship patterns. The modeling approach first integrates ship trajectories from automatic identification system (AIS) historical data, then categorizes different vessels’ navigation behaviors, and introduces a visual-oriented framework to characterize and highlight such patterns. The potential of the model is illustrated by a case study applied to the Jiangsu and Zhejiang waters in China. The results show that the CSBP mining model can highlight complex ships’ behavioral patterns over long periods, thus providing a valuable environment for supporting ship traffic management and preventing maritime accidents.

Development of an Electronic Smart Safe Box Using Private Blockchain Technology

Effective security has become a vital part of our daily routine, for example securing cash, jewelry, documents, and other valuables. One way is to protect these items by keeping them in a safe box. However, current safe boxes are vulnerable to a number of security attacks such as physical, repudiation, anonymously modifying, etc. In this regard, this paper presents a robust smart safe box using the blockchain solution to ensure each transaction and secure it from unauthorized access. Also, it traces back the local safe items by reading back to blockchain data transactions and protecting them from thieves by tracking the burglar with a real-time tracking location Global Positioning System (GPS) tracker and capturing a picture and video recording through the external camera. Similarly, with more security enhancement using the TensorFlow framework to analyze optical detection and identify things inside the box using an internal camera. At the same time, each transaction is stored by using Write Once and Read Many (WORM) patterns saved in local private blockchain data block logs. Finally, it uses three-way authentication mechanisms to unlock the safe box that authenticates the users, (1) face recognition, (2) fingerprint and (3) the keypad password. The performance of the proposed smart safe box is tested by a number of hardware and blockchain tests. The results reveal that the proposed smart safe box ensures safe and authenticated access to the box and records each transaction using blockchain technology. Further, the performance evaluation of the private blockchain significantly improved the integrity and safeness of the proposed smart safe box.

Multi-site calibration and validation of a wide-angle ultrasonic sensor and precise GPS to estimate pasture mass at the paddock scale

Ultrasonic sensors are a tool for pasture biomass measurement but there has been a lack of on-farm validation to prove their practicality and applicability. As a result, uptake of such sensors is still low. This study sought to perform a multi-site and multi-season calibration and validation of a wide-angle, vehicle-mounted, ultrasonic sensor gathering data while the unit was used in motion on seven commercial dairy farms. This was facilitated by the integration of the ultrasonic sensor, a Real-Time Kinematic Geographic Positioning System (RTK-GPS), and a datalogger into a single system that was attached via a custom bracket to the bull bar of an all-terrain vehicle. The resulting dataset was gathered over a 12-month period, and comprised 1886 reference biomass values from destructive cutting of perennial ryegrass pastures, accompanied by ultrasonic sensor height readings and compressed height readings from a rising plate meter (RPM): a tool that is widely used on commercial farms. The final root mean square errors of prediction (RMSEP) were 1034 and 904 kg DM/ha for Ultrasonic sensor and RPM respectively across all sites and seasons. These were considered high in comparison to previous literature, but this could be attributed to the very broad applicability of the models created. It was also demonstrated that the ultrasonic instrument could be used to create accurate biomass maps for paddocks in the same time taken to survey a whole paddock by RPM, a method that only results in a single paddock average value. Considering the ultrasonic sensor was only slightly less precise and accurate than the RPM at estimating biomass, but considerably more functional in its ability to determine within paddock biomass variation, it was concluded that the ultrasonic sensor was the more informative option.

GPS data Mining at Signalized Intersections for Congestion Charging.

Nowadays more private car trips have caused worse congestion due to the Covid-19 pandemic in many cities. Congestion charging is one of the taxes that is levied on vehicle owners to reduce urban traffic congestion. One of the most important reasons congestion charging is not accepted by the public is the high cost. Monitoring the state of traffic congestion in real time requires a lot of expensive installations. The purpose of this paper is to make congestion charging more accurate and acceptable using artificial intelligent algorithm. Massive real-time Global Positioning System (GPS) data provides new data for road congestion charging. The queuing length at intersections is an important measurement for the degree of traffic congestion, and it is also the basis for road congestion pricing. GPS positioning cannot provide sufficient position accuracy for lane identification of vehicles. In this study, a comprehensive model consisting of a real-time lane identification model and a real-time queue length estimation model is developed based on the traffic shockwave theory using GPS data. The comprehensive model can identify the lane where the queuing vehicle is located and estimate the real-time queue length of the lane. The proposed models were evaluated using field-collected data in Guangzhou, China. The testing results show that the proposed comprehensive model can identify lanes and estimate queue lengths with satisfactory accuracy. The model proposed in this paper provides real-time data for road dynamic pricing in a cost-effective way, which can promote the implementation of congestion charging in cities.

Real-Time Precise GPS Orbit and Clock Estimation With a Quasi-Orbit-Fixed Solar Radiation Pressure Model

<h3>Abstract</h3> Real-time precise global navigation satellite system (GNSS) orbit and clock products play a key role for real-time GNSS-based applications, both in the scientific and industrial communities. Different from the typical two-step procedure to generate orbit and clock solutions separately, we estimate the real-time orbit and clock products simultaneously using a Kalman filter. For this purpose, we developed a GNSS data processing software that can run in pseudo-real-time mode with RINEX files and is ready to run in real-time mode once given the real-time observation stream. Meanwhile, a quasi-orbit-fixed solar radiation pressure (SRP) model is developed. In order to verify the performance of the software and the new SRP model, several experiments with a global network of 60 tracking stations over a time span of three months were conducted to generate real-time Global Positioning System (GPS) orbit and clock products. Then, the results were assessed in terms of accuracy and efficiency, both critical for real-time precise GNSS applications. Compared to the International GNSS Service (IGS) final orbits, the real-time GPS orbit accuracy was 2.82 cm, 5.45 cm, and 5.47 cm in the radial, along-track, and cross-track components, respectively. The precision of the clock product in terms of standard deviation (STD) value was about 0.1 ns. Moreover, the average execution time per epoch was usually less than 1.0 s, which ensures the high efficiency of the processing.

DMD-based image-free system for real-time detection and positioning of point targets

The detection and positioning system of point targets has critical applications in many fields. However, its spatial and temporal resolution is limited for the image-based system due to a large amount of data. In this work, an image-free system with less data and high update rate is proposed for the detection and positioning of point targets. The system uses a digital micromirror device (DMD) for light modulation and a pixel array as the light intensity detector, and the DMD is divided into multiple blocks to selectively acquire the intensity information in the region of interest. The centroid position of a point target is calculated from the intensity on the adjacent rows or columns of the micromirror. Simulation indicates that the performance of the proposed method is close to or better than that of the traditional methods. In static experiments, the centroiding accuracy of the proposed system is about 0.013 pixel. In dynamic experiments, the centroiding accuracy is better than 0.07 pixel in the condition of signal-to-noise ratio (SNR) greater than 35.2 dB. Meanwhile, the built system has an update rate of 1 kHz in the range of 1024×768 pixels, and the method acquires only 8 bytes of data for one-time positioning of a point target, making it applicable to real-time detection and positioning of point targets.

Demonstration and verification experiment in deep space optical communications

In deep space optical communications, pointing, acquisition and tracking (PAT) system need to locate the position of the beacon, precisely. The way of employing the natural celestial body as the beacon come to draw attention, in contrast to regarding the laser as the beacon which has a series of problems of limited power, serious attenuation, high cost and difficult design realization. In this paper, the primary goal is to study the problem of precise positioning for the beacon by taking the natural celestial body as the beacon. Given the long distance, with small target and large uncertain region in deep space optical communications, the location of the beacon can be realized by the combination of rough and precise positioning based on the image feature of the beacon. Firstly, it performs the rough positioning according to the image characteristic information of the beacon’s edge, then in the uncertain region, the processing scope has been narrowed according the results of the rough positioning, finally the beacon is precisely located based on its minutiae feature by the way of feature point matching to achieve the position of the beacon. The impact due to the autorotation of celestial body on the image transform has been also discussed and analyzed with the mapping relationship of coordinate and geometric transform. The feasibility of the algorithm is verified by a long distance far-field Earth–Moon experiment. The design of system implementation on embedded hardware system can provide a reference for the real-time positioning system in deep space optical communications.

Research on BeiDou Satellite Positioning Algorithm Based on GPRS Technology

The BeiDou Satellite Navigation System of China can provide users with high precision, as well as all-weather and real-time positioning and navigation information. It can be widely used in many applications. However, new challenges appear with the expansion of the 5G communication system. To eradicate or weaken the influence of various errors in BeiDou positioning, a BeiDou satellite positioning algorithm based on GPRS technology is proposed. According to the principles of the BeiDou Satellite navigation system, the navigation and positioning data are obtained and useful information are extracted and sent to the communication network through the wireless module. The error is corrected by establishing a real-time kinematic (RTK) mathematical model, and the pseudorange is calculated by carrier phase to further eliminate the relativistic and multipath errors. Based on the results of error elimination, the BeiDou satellite positioning algorithm is improved and the positioning error is corrected. The experimental results show that the positioning accuracy and efficiency of the algorithm can meet the actual needs of real-time dynamic positioning systems.

Research on Visualized Traceability Supervision System of Medical Equipment Based on Wireless Local Area Network Real-time Positioning System

Aiming at the shortcomings of traditional medical device tracking and supervision mode, such as delayed feedback of medical device positioning information, poor visual tracking effect of medical device, and missing early warning of medical device working state supervision, a medical device visual tracking and supervision system based on wireless Local Area Network (LAN) real-time positioning system is developed. Introduction of wireless LAN real-time positioning system, using standard coding algorithm generating device object ID tags, with the only attribute based on RFID read-write terminal complete physical mapping of medical equipment and material ID label, we realize a real-time positioning and traceability of medical equipment, with the help of active warning algorithm based on depth of learning medical equipment working state supervision for early warning, with the help of indoor map generation and trajectory playback medical equipment to realize visualization back regulation. Choose equipment of hospital of Linyi Central Hospital for performance evaluation of the carrier, the evaluation model of engineering practice and clinical statistical analysis, the results show that the system has a complete real-time positioning, visible trace, abnormal warning function. In medical equipment real-time positioning information visual tracing feedback, abnormal state warning etc, medical equipment has obvious advantages.

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.

Real-time RSS-based positioning system using neural network algorithm

Locating services have come under the spotlight in recent years in various applications. However, locating methods that use received signal strength have low accuracy due to signal fluctuations. For this purpose, we present a Wi-Fi based locating system using artificial neural network to enhance the positioning process performances. We optimized the Levenberg Marquardt algorithm to propose the better configuration of the multi-layer time-delay perception neural network. We achieved an average error of 10.3 centimeters with a grid of 0.4 meter in four tests. Yet, due to the instability of the received signal strength RSS-based locating systems present a limitation in the resolution finesse that depends on the grid size.

Opportunities to Apply Precision Livestock Management on Rangelands

Precision livestock management has become a new field of study as the result of recent advancements in real-time global positioning system (GPS) tracking, accelerometer and other sensor technologies. Real-time tracking and accelerometer monitoring has the potential to remotely detect livestock disease, animal well-being and grazing distribution issues and notify ranchers and graziers so that they can respond as soon as possible. On-going research has shown that accelerometers can remotely monitor livestock behavior and detect activity changes that are associated with disease and parturition. GPS tracking can also detect parturition by monitoring the distance between a ewe and the remainder of the flock. Tracking also has the potential to detect water system failures. Combinations of GPS tracking and accelerometer monitoring may be more accurate than either device used by itself. Real-time GPS tracking can identify when livestock congregate in environmental sensitive areas which may allow managers the chance to respond before resource degradation occurs. Identification of genetic markers associated with terrain use, decreased cost of GPS tracking and novel tracking data processing should facilitate development of tools needed for genetic selection for cattle grazing distribution. Precision livestock management has potential to improve welfare of livestock grazing rangelands and forested lands, reduce labor costs and improve ranch profitability and improve the condition and sustainability of riparian areas and other environmental sensitive areas on grazing lands around the world.

Two-dimensional flood model for risk exposure analysis of land use/land cover in a watershed.

BACKGROUND AND OBJECTIVES: The study involved developing a two-dimensional flood model to analyze the risk exposure of land use/land cover based on the generated flood hazard maps for the six return period scenarios in the Solana watershed.METHODS: The approach consisted of applying hydrologic and hydraulic numerical flood models and the suite of advanced geographic information systems and remote sensing technologies. The process involved utilizing a high-resolution digital elevation model and a set of high-precision instruments such as the real-time kinematic-global position system receiver, digital flow meter, deep gauge, and automatic weather station in collecting the respective data on bathymetry, river discharge, river depth, and rainfall intensity during a particular climatic event, needed for the model development, calibration and validation.FINDINGS: The developed two-dimensional flood model could simulate flood hazard with an 86% accuracy level based on the coefficient of determination statistics. The flood risk exposure analysis revealed that coconut is the most affected, with 31.3% and 37.1% being at risk across the 2-year and 100-year return period scenarios, respectively. Results also showed that rice and pineapple are at risk of flooding damage with the increasing rate of exposure by a magnitude of 42.9 and 9.3 across the 2-year and 100-year flood scenarios, respectively.CONCLUSION: The study highlighted the integration of the findings and recommendations in the localized comprehensive land use plan and implementation to realize the challenge of building a climate change proof and a flood-resilient human settlement in the urbanizing watershed of Solana.

High precision positioning system for autopilot based on multi-sensor fusion

For autonomous vehicles, autonomous positioning is a core technology in their development. A good positioning system not only helps them efficiently complete autonomous operations, but also improves safety performance. At present, the use of global positioning system (GPS) is a more mainstream positioning method, but in indoor, serious shelter and other environments, GPS signal loss will lead to positioning failure. In order to solve this problem, this paper proposes a method of mapping before positioning, and designs a set of high precision real-time positioning system by combining the technology of multi-sensor fusion. The designed system was carried on a Wuling sightseeing bus, and the mapping and positioning tests were carried out in the Nanhu Campus of Wuhan University of Technology, the East Campus of Mafangshan Campus and the underground garage where GPS signals were lost. The test results show that the system can realize the high precision real-time positioning function of the autonomous vehicle. Therefore, the in-depth study and implementation of this system is of great significance to the promotion and application of the automatic driving industry.

An SVM Based Weight Scheme for Improving Kinematic GNSS Positioning Accuracy with Low-Cost GNSS Receiver in Urban Environments.

High-precision positioning with low-cost global navigation satellite systems (GNSS) in urban environments remains a significant challenge due to the significant multipath effects, non-line-of-sight (NLOS) errors, as well as poor satellite visibility and geometry. A GNSS system is typically implemented with a least-square (LS) or a Kalman-filter (KF) estimator, and a proper weight scheme is vital for achieving reliable navigation solutions. The traditional weight schemes are based on the signal-in-space ranging errors (SISRE), elevation and C/N0 values, which would be less effective in urban environments since the observation quality cannot be fully manifested by those values. In this paper, we propose a new multi-feature support vector machine (SVM) signal classifier-based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The proposed new weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. To validate the performance of the newly proposed weight scheme, we have implemented it into a real-time single-frequency precise point positioning (SFPPP) system. The dynamic vehicle-based tests with a low-cost single-frequency u-blox M8T GNSS receiver demonstrate that the positioning accuracy using the new weight scheme outperforms the traditional C/N0 based weight model by 65.4% and 85.0% in the horizontal and up direction, and most position error spikes at overcrossing and short tunnels can be eliminated by the new weight scheme compared to the traditional method. It also surpasses the built-in satellite-based augmentation systems (SBAS) solutions of the u-blox M8T and is even better than the built-in real-time-kinematic (RTK) solutions of multi-frequency receivers like the u-blox F9P and Trimble BD982.

Validation of UWB positioning systems for player tracking in tennis

Precise and reliably tracking of players’ position during the action or during the game is important in many sports. Our motivation was to investigate and validate the use of ultra-wideband (UWB) systems for various player and coach applications that depend on tracking the position of the player and possible related events. We developed and implemented a real-time positioning system that allows (a) the validation of UWB positioning and (b) a test application to support tennis game strategy, which monitors the trajectory of players and analyzes their position frequency in different tennis court areas at the moment of ball impact. The accuracy of the UWB positioning system is verified by the professional high-precision optical tracking system Qualisys. The validation of our UWB system, which operates in a dynamic mode in open space, was performed at distances between the devices in the range of 4.7 to 14.9 m. The measured distance errors between the tag and two anchors show mean values of 0.1 cm and 1.8 cm and variances of 14.9 cm and 14.3 cm. The absolute position errors on the tennis court have mean values of 14.2 cm and a variance of 16.1 cm. The position error limits for the confidence intervals of 50%, 90%, and 95% are 10.2 cm, 25.6 cm, and 33.4 cm, respectively. The developed application helps trainers to organize and perform training exercises by providing accurate analysis results that are otherwise very difficult to obtain. At impact, the absolute position error is slightly smaller due to the lower speed of the player; the average measured position error at impact is therefore 11.3 cm. The results of the field tests confirm the usability of the UWB system for position tracking in tennis, as well as in other similar sports applications.

Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology

An accurate positioning and attitude computation of vehicles, robots, or even persons is of the utmost importance and critical for the success of many operations in multiple commercial, industrial, and research areas. However, most of these positioning and attitude systems rely on inertial measurement units that must be periodically recalibrated and have a high cost. In the present work, the design of a real-time positioning and attitude system using three positioning sensors based on the GNSS-RTK technology is presented. This kind of system does not need recalibration, and it allows one to define the attitude of a solid by only computing the position of the system in the global reference system and the three angles that the relative positions of the GNSS antennas define with respect to the principal axes of the solid. The position and attitude can be computed in real time for both static and dynamic scenarios. The only limitation of the system is that the antennas need to be in open air to work at full performance and accuracy. All the design phases are covered in the prototype construction: requirement definition, hardware selection, software design, assembly, and validation. The feasibility and performance of the system were tested in both static and dynamic real scenarios.

Sustainability of Public Transportation: An Examination of User Behavior to Real-Time GPS Tracking Application

Public transportation is an effective method of mobility that promotes cost-saving and is environmentally friendly. Poor public transport ridership in Malaysia is due to the unsatisfactory attitude of public transport users and inaccurate information on departure and arrivals. Sarawak, a state of Malaysia, is especially poor in ridership of public transport. A real-time Global Positioning System (GPS) tracking application (app) was found to be an effective tool to increase the ridership of public transport. Hence, a mobile app named UniBus was developed to enhance the ridership of public transport in Sarawak. The determinants that affect satisfaction and customer loyalty such as accessibility, reliability, comfort, safety, and security were all examined before and after the use of real-time GPS tracking app. The data was collected in Kuching, and targeted public transport users who used the UniBus app. The result indicated that all the mentioned variables were improved after using a real-time GPS tracking app. It is suggested that future studies can consider other factors such as service quality, availability, and perceived value as well as cover other states of Malaysia.

Strawberry Plant Alive Status Detection and Relative Pixel Based Plant Localization

Abstract Autonomous plant alive status monitoring and corresponding localization of individual plant are two important tasks in precision agriculture. In this study, two new methods that are crucial to such robotic operations are proposed. First, a low cost and light scene invariant approach is proposed to differentiate green and yellow leaves using distinct color-ratio index ranges. Second, based on the relative pixel information of neighboring plants, an extended Kalman filter is used to determine plant positions. Such a differential style localization method is shown to be capable of achieving a similar centimeter level accuracy as light detection and ranging (LIDAR) or real-time kinematic-global positioning system (RTK-GPS) based approaches, but with a much lower upfront and maintenance cost. These two new methods are successfully validated in a nearby commercial field.