Global Positioning System Information Research Articles

An Image Processing Approach for Real-Time Safety Assessment of Autonomous Drone Delivery

The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in the delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image-processing-based classification approach for the delivery drone dropping process at a predefined destination. It employs live streaming via a single onboard camera and Global Positioning System (GPS) information. A two-stage processing procedure is proposed based on image segmentation and classification. Relevant parameters such as camera parameters, light parameters, dropping zone dimensions, and drone height from the ground are taken into account in the classification. The experimental results indicate that the proposed approach provides a fast method with reliable accuracy based on low-order calculations.

Application of “3S” Technology in Land Resource Management

Land resource management is one of the most important ways to deal with the relationship between land resource and human being to improve land use efficiency and gain more benefits. “3S” technology refers to remote sensing (RS), global positioning system (GPS) and geological information system (GIS). “3S” technology is widely used in different fields generally as an excellent method and it has advantages to exploit in land resource management. Now as the population grows, the conflict between land resource and mankind extrudes with global resource shortage. The effort using “3S” cannot be ignored. This article explains the definition and ability of “3S” technology and analyze the application of “3S” in the Investigation of land resource, land environment dynamic monitoring, land evaluation, land consolidation and land planning. The author concludes the utilization of “3S” technology in land resource management and looks forward its development in the future from different perspectives. Informatization, intelligent and integration are three directions “3S” will make progress. This article takes some examples to illustrate how to help “3S” technology step forward in the future. Through these ways, the optimized “3S” technology has ability to overcome disadvantages and build up new use of the way. So the efficiency of land resource management will be improved obviously to achieve the sustainable use goal of land resource.

Digital technology and its application in supply chain management: new evidence from China's economy.

The purpose of this article is to investigate the influence that practices using information technology (IT) have on the development of a competitive advantage across the supply chain. An organization has a competitive advantage when it has qualities that give the required foundations for it to separate itself from other organizations that are also in its industry. Pressure is applied to the corporate environment as a result of competition and ongoing changes, such as the introduction of new products and technical advancements, the decline of product lifestyles, and the proliferation of products. In order to maintain a competitive edge and achieve financial success in business, organizations are necessary for responding to changes in the market. Through the use of supply chain markets, companies are able to react quickly to unforeseen shifts in the market, and these shifts may be turned into lucrative business possibilities. One of the most significant things that firms can do to assist themselves is make use of information technology to improve their supply chain management agility. From March 2021 through January 2022, the area of China will have a total sample size of 247 persons fill out a questionnaire as part of the data collection process. In each and every questionnaire, the measurements were taken using a Likert scale with five points. The partial least square-structural equation modeling (PLS-SEM) approach is used to the causal model in order to assess the model's reliability and validity. This technique is used to evaluate the causal model. The findings indicate that information technology has a favorable impact on the adaptability of supply chain management systems. In addition, the findings that were collected have shown that there are four factors that influence the SCM systems. These factors are the IT skills and knowledge, the integration of IT-based systems, the IT infrastructure, and the design of global position system and geographic information systems. In addition, this research offers practitioners recommendations for implementing digital technology for supply chain management and building suitable business strategies at various stages of digitalization.

Assessment of Soil Fertility through GIS Techniques and Thematic Mapping in Rice Growing Areas of Jagtial District in Telangana State, India

Survey was conducted in Jagtial district of Telangana state to identify the soil fertility status of rice growing areas. Soil fertility is one of the major limiting factor for maintaining sustainability in crop yields. Totally 90 surface soil samples collected from the selected rice cultivated farmers’ field. Collected soil samples were tested for texture, pH, EC, organic carbon, available N, P, K and S in order to identify the nutrient status in soils and develop the corrective measures. The soil fertility maps generated through Global Positioning System (GPS) and Arc Geographic Information System (GIS) software to formulate balanced fertilizer recommendations and to understand the spatial variability of nutrients in soil. Survey results showed that the sand was ranged from 44 to 80% and that of silt was 3 to 20 % while the range of clay was 13 to 42% and varied three textural classes viz., sandy clay loam, sandy loam and loamy sand. Soil pH was neutral to slightly alkaline (6.8 to 8.2), electrical conductivity rated as non-saline (0.23 to 0.69 dS m-1), organic carbon was low-medium (0.3 to 0.6%), available-N was low (101 to 195 kg ha-1), available-P was high (26.8 to 61.5 kg ha-1), available-K was medium to high (120 to 449 kg ha-1) and available-S was medium to high (10.0 to 26.8 mg kg-1). Among these organic carbon (%) and available-N were important constraints in soil fertility in rice growing areas of Jagtial district of Telangana state.

Visual Place Recognition of Robots via Global Features of Scan-Context Descriptors with Dictionary-Based Coding

Self-localization is a crucial requirement for visual robot place recognition. Particularly, the 3D point cloud obtained from 3D laser rangefinders (LRF) is applied to it. The critical part is the efficiency and accuracy of place recognition of visual robots based on the 3D point cloud. The current solution is converting the 3D point clouds to 2D images, and then processing these with a convolutional neural network (CNN) classification. Although the popular scan-context descriptor obtained from the 3D data can retain parts of the 3D point cloud characteristics, its accuracy is slightly low. This is because the scan-context image under the adjacent label inclines to be confusing. This study reclassifies the image according to the CNN global features through image feature extraction. In addition, the dictionary-based coding is leveraged to construct the retrieval dataset. The experiment was conducted on the North-Campus-Long-Term (NCLT) dataset under four-seasons conditions. The results show that the proposed method is superior compared to the other methods without real-time Global Positioning System (GPS) information.

BT26 Safe use of mobile photographic devices in dermatology: a multicentre quality-improvement project

Abstract Mobile devices are increasingly being used in dermatology to take clinical images, particularly in settings where medical photography services are not readily available. Healthcare professionals are expected to use mobile technology safely to improve patient care. Published UK guidance on the use of mobile photographic devices in dermatology details standards in three key areas: gaining patients’ informed consent, safe use of mobile devices to capture patient images, and safe transfer and storage of these images. A quality-improvement project was conducted across three UK centres. Baseline data were collected over a 2-week period in January 2023 using an anonymous staff survey and analysis of 50 outpatient encounters where mobile devices were used to take clinical images. Our survey achieved a response rate of 77% (n = 20) from consultants (60%), specialty doctors/fellows (30%) and nurses/advanced care practitioners (10%). All respondents sought patient consent prior to taking images on their personal devices, with only 10% using written consent forms (remainder using verbal consent). Thirty-five per cent reported always documenting consent in patient notes and only 20% discussed the risks and benefits of using mobile devices to take patient images. Eighty-five per cent used devices with strong passcodes (six or more characters), but only 50% confirmed that data encryption was enabled on the device. Thirty-five per cent disabled cloud-based back-up systems, 55% disabled Bluetooth and only 5% switched off global positioning system information before taking patient images. For image transfer, 80% used NHS.net email and 20% used a secure clinical image transfer application. Seventy per cent confirmed using a secure network server for image storage and that they always removed patient images from their device after transfer. Analysis of 50 patient encounters revealed that verbal consent was only documented in 8% of cases and a discussion around the risks and benefits of using mobile devices was not documented in any case. National guidance states that it is the healthcare professional’s responsibility to give patients clear information on the risks and benefits of using mobile devices to capture and transfer clinical images for storage, without which there is no informed consent. Our findings highlight a lack of awareness and adaptation of practice to these standards. We will ascertain reasons for this and implement interventions to meet guidance, including an educational meeting and introduction of a standardized written consent form to ensure patient safety and appropriate informed consent. We will present our findings on the impact of these interventions following subsequent rounds of data collection.

A Robotized Raspberry-Based System for Pothole 3D Reconstruction and Mapping

Repairing potholes is a task for municipalities to prevent serious road user injuries and vehicle damage. This study presents a low-cost, high-performance pothole monitoring system to maintain urban roads. The authors developed a methodology based on photogrammetry techniques to predict the pothole’s shape and volume. A collection of overlapping 2D images shot by a Raspberry Pi Camera Module 3 connected to a Raspberry Pi 4 Model B has been used to create a pothole 3D model. The Raspberry-based configuration has been mounted on an autonomous and remote-controlled robot (developed in the InfraROB European project) to reduce workers’ exposure to live traffic in survey activities and automate the process. The outputs of photogrammetry processing software have been validated through laboratory tests set as ground truth; the trial has been conducted on a tile made of asphalt mixture, reproducing a real pothole. Global Positioning System (GPS) and Geographical Information System (GIS) technologies allowed visualising potholes on a map with information about their centre, volume, backfill material, and an associated image. Ten on-site tests validated that the system works in an uncontrolled environment and not only in the laboratory. The results showed that the system is a valuable tool for monitoring road potholes taking into account construction workers’ and road users’ health and safety.

GEO-LOCATING AN IMAGE USING EXIF DATA

GPS (Global Positioning System) information is a type of EXIF (Exchangeable Image file Format) metadata that's implanted in a computerized photo record. The project aims to create a system that takes useruploaded pictures, extracts the metadata, shows the coordinates of the location information on a custom Google Maps embedded API, and displays the make and model of the gadget utilized to capture the picture. The system employs Python's exif and Pillow modules to extract the metadata from the uploaded image file. The metadata incorporates GPS information that contains the latitude and longitude coordinates of the area where the picture was taken. The extracted coordinates are then passed to the Google Maps API to show the area of the picture on map. This data is shown on the webpage, giving extra subtle elements almost the picture.

Spatial distribution of common childhood illnesses, healthcare utilisation and associated factors in Ethiopia: Evidence from 2016 Ethiopian Demographic and Health Survey.

Childhood illnesses, such as acute respiratory illness, fever, and diarrhoea, continue to be public health problems in low-income countries. Detecting spatial variations of common childhood illnesses and service utilisation is essential for identifying inequities and call for targeted actions. This study aimed to assess the geographical distribution and associated factors for common childhood illnesses and service utilisation across Ethiopia based on the 2016 Demographic and Health Survey. The sample was selected using a two-stage stratified sampling process. A total of 10,417 children under five years were included in this analysis. We linked data on their common illnesses during the last two weeks and healthcare utilisation were linked to Global Positioning System (GPS) information of their local area. The spatial data were created in ArcGIS10.1 for each study cluster. We applied a spatial autocorrelation model with Moran's index to determine the spatial clustering of the prevalence of childhood illnesses and healthcare utilisation. Ordinary Least Square (OLS) analysis was done to assess the association between selected explanatory variables and sick child health services utilisation. Hot and cold spot clusters for high or low utilisation were identified using Getis-Ord Gi*. Kriging interpolation was done to predict sick child healthcare utilisation in areas where study samples were not drawn. All statistical analyses were performed using Excel, STATA, and ArcGIS. Overall, 23% (95CI: 21, 25) of children under five years had some illness during the last two weeks before the survey. Of these, 38% (95%CI: 34, 41) sought care from an appropriate provider. Illnesses and service utilisation were not randomly distributed across the country with a Moran's index 0.111, Z-score 6.22, P<0.001, and Moran's index = 0.0804, Z-score 4.498, P< 0.001, respectively. Wealth and reported distance to health facilities were associated with service utilisation. Prevalence of common childhood illnesses was higher in the North, while service utilisation was more likely to be on a low level in the Eastern, South-western, and the Northern parts of the country. Our study provided evidence of geographic clustering of common childhood illnesses and health service utilisation when the child was sick. Areas with low service utilisation for childhood illnesses need priority, including actions to counteract barriers such as poverty and long distances to services.

Spatio-Temporal Digraph Convolutional Network-Based Taxi Pickup Location Recommendation

The recommendation of taxi pickup locations plays an important role for drivers in carrying passengers efficiently. In addition, the emergence of the Internet of Vehicles provides technical support for it. However, existing recommendation methods do not model dynamic global positioning system information well and in real-time. In this article, we propose a spatio-temporal digraph convolutional network (STDCN) model. First, the pickup and drop-off locations are modeled into a directed spatio-temporal graph as input to the model. The correlation between each node is calculated as a unified edge weight based on the gray relational analysis. Then, the STDCN is used for dynamic spatio-temporal feature extraction. Finally, the edge-cloud collaboration framework is adopted to recommend local taxi pickup locations in real-time. The experimental results show that the proposed method is better than competing methods in terms of effectiveness and efficiency, and it shows good industrial conversion application prospects.

Geographic intelligence to investigate community participation: a scoping review

Purpose To understand the applicability and unique contributions of global positioning system (GPS) and geographic information system (GIS) technologies to investigate individuals’ participation in the community. Methods A scoping review was conducted to summarize studies that employed GPS and/or GIS to assess community participation. Systematic literature searches were performed using nine electronic databases, up to April 2022. Two independent reviewers screened studies for eligibility and extracted data from the selected studies. Results The searches retrieved 628 articles, of which 36 met the inclusion criteria. In 31 studies, a GPS unit or GPS-enabled device monitored the community mobility of individuals with different health conditions. Tracking periods varied from five hours to 30 consecutive days. The spatiotemporal parameters obtained from satellite-based data provided information about individuals’ presence in the community. Most studies combined GPS with other measuring tools (self-report diaries/questionnaires, qualitative interviews) to capture a broader description of community participation. Conclusions GPS and GIS are viable approaches for advancing research as they provide unique information about community participation not easily captured by other methods. The combination of available methods comprehensively address the physical and social dimensions of this construct. IMPLICATIONS FOR REHABILITATION Global positioning system (GPS) and geographic information system (GIS) technology allows direct and real-time quantification of patterns of individuals’ community mobility. The geographic area where individuals participate in daily living activities complements information from self-report diaries/questionnaires. Combining GPS technology with self-report diaries/questionnaires allows a broader description of individuals’ community participation, addressing respectively both its physical and social dimensions. Knowledge of individuals’ access to and involvement in community locations/situations may help therapists design interventions to improve community participation.

Landmark-Based Scale Estimation and Correction of Visual Inertial Odometry for VTOL UAVs in a GPS-Denied Environment.

With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and are applied in many areas. However, there are some environments where the Global Positioning System (GPS) is unavailable or has the problem of GPS signal outages, such as indoor and bridge inspections. Visual inertial odometry (VIO) is a popular research solution for non-GPS navigation. However, VIO has problems of scale errors and long-term drift. This study proposes a method to correct the position errors of VIO without the help of GPS information for vertical takeoff and landing (VTOL) UAVs. In the initial process, artificial landmarks are utilized to improve the positioning results of VIO by the known landmark information. The position of the UAV is estimated by VIO. Then, the accurate position is estimated by the extended Kalman filter (EKF) with the known landmark, which is used to obtain the scale correction using the least squares method. The Inertial Measurement Unit (IMU) data are used for integration in the time-update process. The EKF can be updated with two measurements. One is the visual odometry (VO) estimated directly by a landmark. The other is the VIO with scale correction. When the landmark is detected during takeoff phase, or the UAV is returning to the takeoff location during landing phase, the trajectory estimated by the landmark is used to update the scale correction. At the beginning of the experiments, preliminary verification was conducted on the ground. A self-developed UAV equipped with a visual-inertial sensor to collect data and a high-precision real time kinematic (RTK) to verify trajectory are applied to flight tests. The experimental results show that the method proposed in this research effectively solves the problems of scale and the long-term drift of VIO.

Using consensus methods to standardise judgement-based guidelines required for player management decision-making processes: A case study in professional rugby union

Standards are pivotal for generating the evidence required to manage players in professional sport environments like rugby union. Resultantly, using a three-step qualitative approach, this study aimed to formulate a consensus as a subjective standard for evidence generation pertaining to player management. The consensus statement intended to identify evidence on peaks/troughs in player external training loads using Global Positioning System (GPS)-based information in the High-Performance Unit (HPU) of a Gallagher Premiership rugby union club. Initially, a systematic review adhering to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework was conducted to unravel the factors considered (literature-based cues) when identifying peaks/troughs in player external training loads using GPS information. Next, thematic analysis conducted on the data obtained from 7 semi-structured interviews with HPU staff highlighted that they consider 6 factors with 38 elements (practitioner-based cues) during player external training load management. Thereafter, guided by the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument and by utilising selected elements representing 4/6 factors (healthy player, GPS information, longitudinal durations and practitioner judgements on information), a consensus among practitioners for identifying peaks/troughs in player external training loads was developed with the participation of five HPU members using the nominal group technique (NGT). Practitioners reached an agreement with regard to 12 indicators to subjectively identify peaks/troughs in player external training loads within the considered environment.

Correction of Range-Variant Motion Error and Residual RCM in Sparse Regularization SAR Imaging.

Lq (0 < q ≤ 1) regularization has been confirmed effective when applied to sparse SAR imaging. However, the inaccuracies caused by motion errors in the observation model will lead to various degradations and defocus in the reconstructed image. For high-resolution and light-small SAR systems, the range-variant motion errors will decrease the accuracy of range cell migration correction (RCMC), and residual range cell migration (RCM) will exceed multiple range resolution cells and degrade the image quality substantially. Aiming at this problem, in this paper, a novel azimuth-range decoupled sparse SAR imaging method with coarse-to-fine range-variant motion errors and residual RCM correction method is proposed. First, a one-step motion compensation (MOCO) operator is proposed using the inertial navigation systems (INS)/global positioning systems (GPS) information, which can significantly reduce the residual RCM and improve the reconstruction accuracy. Second, a fine high-order phase-error correction method is performed to correct the range and cross-range-varying phase errors using a joint imaging and phase-error estimation scheme, which will further improve the image focusing quality. Experimental results indicate the effectiveness of the proposed method.

Modelling the Energy Consumption of Driving Styles Based on Clustering of GPS Information

This paper presents a novel approach to distinguishing driving styles with respect to their energy efficiency. A distinct property of our method is that it relies exclusively on the global positioning system (GPS) logs of drivers. This setting is highly relevant in practice as these data can easily be acquired. Relying on positional data alone means that all features derived from them will be correlated, so we strive to find a single quantity that allows us to perform the driving style analysis. To this end we consider a robust variation of the so-called "jerk" of a movement. We give a detailed analysis that shows how the feature relates to a useful model of energy consumption when driving cars. We show that our feature of choice outperforms other more commonly used jerk-based formulations for automated processing. Furthermore, we discuss the handling of noisy, inconsistent, and incomplete data, as this is a notorious problem when dealing with real-world GPS logs. Our solving strategy relies on an agglomerative hierarchical clustering combined with an L-term heuristic to determine the relevant number of clusters. It can easily be implemented and delivers a quick performance, even on very large, real-world datasets. We analyse the clustering procedure, making use of established quality criteria. Experiments show that our approach is robust against noise and able to discern different driving styles.

Assessment of the Spatial Distribution and Mapping Soil Physico-Chemical Properties of Nalgad Municipality, Jajarkot, Nepal

A detailed soil inventory study using Global Positioning System (GPS) and Geographical Information System (GIS) was conducted in Nalgad Municipality of Jajarkot, Nepal in 2019. A preliminary reconnaissance survey during pre–field activities and detail field work was carried out to study the soil type and physico-chemical properties based on the soil pit. A total of 51 soil pits were taken in the field representing varied micro topography. Soil Sample pits covering all the units were dug based on the interpreted soil map, topographical map, ZY-3 Satellite imagery for determination of soil profile. Soil classification of the area was done based on the USDA soil taxonomy and the dominant soil orders found in the region were Entisols (15.13 %) and Inceptisols (83.60%). A total of 51 geo-referenced composite soil samples from a depth of 0-20 cm was collected from each pit and analyzed in laboratory for texture, soil pH, soil organic matter, total nitrogen, available phosphorus and exchangeable potassium. Majority of the soil are loam and loamy sand type. There is very low to high level of organic matter present in the study area with more proportion of land under high range of organic matter (64.98%). Total Nitrogen content in soil of the study area ranges from very low to very high level with high level of Nitrogen (65.51%) in major proportion. Low to very high level of available phosphorous content was found in the study area with the dominance of very high level of phosphorous (71.40%). Exchangeable Potassium level in the study area is very high to low. Around 42% land have very high level of potassium. From the soil test result, major nutrients status in soil were found to be good but integrated land management practices should be encouraged for improving land productivity. The generated soil maps may be helpful to stakeholders for planning, monitoring and evaluating the soil status for effective agricultural production.

A New Strategy to Enhance the Security of GPS Location by PGP Algorithm in Smart Containers

Dynamic navigation devices like Global Positioning Systems (GPSs) are deployed for various purposes in different areas and these devices are usually the central point of interest of various groups like hackers to exploit the data sent and received by GPS systems. The GPS data is usually manipulated using spoofing attacks. This paper proposes a robust solution to the spoofing attacks carried out to manipulate, control and modify the location sharing of smart containers. The primary focus of this paper is securing the GPS information shared by the smart containers. The location shared by the smart containers is secured by encrypting it with Pretty Good Privacy (PGP) algorithm to avoid spoofing attacks in particular. The encrypted GPS location is sent across any communication channel. The receiver side will decrypt the encrypted GPS location at the receiving end. Hence, using this method of PGP encryption will ensure the safe and secure sharing of GPS location by the smart containers. As a result, GPS security has been improved by 80%.

Improve the accuracy of vehicle velocity estimation based on low-cost GPS/IMU by GPS course angle

Vehicle velocity is an important parameter for vehicle dynamics control. Global Positioning System (GPS) and inertial measurement unit (IMU) information can be used for velocity estimation by taking advantage of each other through fusion. However, if GPS velocity is used as the only measurement like classic GPS/INS, it will cause a large velocity estimation error or even divergence. This paper, from the perspective of system observability, proves that the GPS course angle (GCA) plays an important role in vehicle velocity estimation based on low-cost GPS/IMU. Firstly, construct the velocity estimation algorithm in the vehicle coordinate system directly. Then, according to the characteristics of different working conditions, the observability of the system without GCA is fully analyzed from the Lie derivative-based observability proof and the observability basic definition. The results show that the weak observability of the yaw angle is the most serious threat in vehicle velocity estimation. After fusing the GCA, the yaw angle potential divergence can be suppressed and the estimation accuracy of velocity will be improved. Even though roll and pitch are still unobservable under some conditions after augmenting GCA, the error propagation analysis shows that their estimation errors have little effect on velocity estimation, so the accurate estimation of velocity can be guaranteed. Finally, the indispensable of GCA in velocity estimation based on low-cost GPS/IMU is verified by Carsim-Simulink joint simulations and vehicle tests.

Research on the Realization Path of Railway Intelligent Construction Based on System Engineering

The implementation of railway intelligent construction is the need of national strategic development and the demand of society. Based on the idea of system engineering, this paper proposes a three-dimensional railway intelligent construction system architecture composed of a full life cycle, management level and technical support. Based on this architecture, a “three-step” implementation path is proposed. Then, it analyzes the technology support framework required in the architecture based on Building Information Modeling (BIM), incorporating Global Positioning System (GPS) and Geographic Information System (GIS), algorithmic prediction and machine learning technology, Internet of Things (IoT) and artificial intelligence technology, big data and cloud computing technology, and the application of railway intelligent construction system architecture is analyzed by taking a railway tunnel project in Zhejiang Province of China as an example. Finally, it discusses the problems that may be encountered in the implementation of railway intelligent construction and puts forward relevant suggestions. The results show that railway intelligent construction is an essential way. At present, China’s railway intelligent construction is still in the primary stage. The design organization should do a good job in the top-level design and accumulate sufficient data for the later stage. All parties in the middle stage of construction should do a good job in the induction and integration of information and accumulate sufficient experience. In this way, we can integrate into the advanced stage and give full play to the advantages of software and hardware integrated applications such as BIM, IoT, big data, cloud computing and intelligent devices so as to truly realize the intellectualization and modernization of railway construction.

Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons

To realize the effective positioning in urban canyons, an enhanced map-aided Global Positioning System (GPS)/three-dimensional (3D) reduced inertial sensor system (RISS) tightly combined positioning strategy is proposed. First, the 3D RISS is only based on the built-in controller area network (CAN). CAN bus sensor without additional sensors is first constructed to lower the cost. Then, a simple but effective enhanced map is created to assist positioning. Based on the map, a Kalman filtering (KF) tightly coupled method is proposed to fuse the 3D RISS with GPS information and to achieve the preliminary positioning. In KF-based preliminary positioning method, a simply observation noise variance optimization algorithm based on 2D enhanced map is proposed to improve KF method. In this algorithm, the value of the observation noise variance matrix is determined only according to the building plane information which is contained in the enhanced map. Further, a multiweight map matching algorithm is proposed for optimizing the initial positioning results. In this algorithm, factors such as distance, direction, road network topology, and lane change are considered and applied to map matching to further increase the positioning performance and form the final positioning results. Finally, the effectiveness of the strategy is proved by field test. The results show that this method has better accuracy and reliability than the conventional method.