جستجوی مقالات مرتبط با کلیدواژه « Global Positioning System » در نشریات گروه « جغرافیا »

Methodology

The present studyinvestigates the effect of gradual reduction of noise from data collected by sensors, accelerometers, magnetometers, gyroscopes, and GPS technology in smartphones on improvement of vehicle positioning. The proposed method is based on using acceleration, azimuth, latitude, longitude and roll angle parameters as an input for the Kalman algorithm and investigates the effect of reducing noise produced by these parameters using the least-squares method onimprovement of the resulting position calculated by the Kalman algorithm. To reach this aim, the roll angle parameter is extracted from the angular Velocity() in y-direction and the azimuth parameter is extracted from the magnetic field() in both x and y directions. These parameters along with the acceleration(a) parameter in x and y directions and the geographic coordinates are selected for the Kalman filtering algorithm. In the proposed method, data received from sensors share common sources of noise produceddue to drift, random movements and bias errors.To reduce this noise independently and systematically, method of averaging with the least-squares is usedfor data produced by each sensor. Thus, noise in the received data is considered as a random parameter and noise reduction is performed based on the percentage of changes in the corrected and observed data in the range of 1 to 10%. Kalman algorithm is implemented for 10 levels of noise reduction and the results areinvestigated and compared.The filter calculates and improves an estimate of position vector x, denoted by  with minimum mean square error using a recursive model. The main objective is to derive an accurate estimate of   for the state of the observed system at time of k. Implementing Kalman filter consists of a prediction step and an updating step. The result is compared todata received from a more accurate reference using RMSE.  

The study area consists of lane no. 2 of the South-North (East-West) Azadegan Highway, Tehran, Iran with a total area of about 26km. Results show that compared to the reference data, using Kalman filter has decreased errorsin positioning the car from 0.8274 m to 0.6763 m with a 2%noise reduction. With a 10% noise reduction, the accuracy of this method has increases to 0.6771 m. This improved accuracy is due to noise reduction and consequently an increase in the correlation between the parameters. Accordingly, the threshold limit for noise reduction and improved positioning using Kalman filter is low and can be recognized by an investigation of a few lowlimits. According to the findings, although reducing the effect of noise can improve positioning with Kalman filter and smart phone sensors, irregular changes in the accuracy of noise reduction methods require determining an optimal percentage for noise reduction.

The remarkable advantage of Global Navigation Satellite Systems (GNSS) is providing navigation service to the user communities, which is independent of the weather condition. The GNSS signals pass through different layers of the Earth's atmosphere. Propagating signals are refracted while passing through every layer and therefore, the reception of the signals is delayed. This delay distorts the accuracy of the position which is computed for a receiver. Troposphere, which is the lower most part of the Earth's atmosphere, is one of the most important source of bias in this respect.
To analyze the impact of tropospheric delay, it is normally divided into dry and wet components. The contribution of the dry and wet parts is reported to be 90 and 10 percent respectively. In contrary to the wet component, the existing models are precise enough to compute the contribution of the dry part on the signal delay. Therefore, analyzing the efficiency of numerical weather models for modeling the tropospheric delay as compared to the existing standard techniques seems to be remarkable.
In this paper, three methods are used for computing the tropospheric error. The first method is based on the Saastamoinen's global troposphere model. This model is commonly used for computing the tropospheric error. Required input parameters are derived from the standard atmosphere model. In the second and third method, ray tracing is used for correcting the GPS measurements. A Numerical Weather Prediction (NWP) model is used for this purpose. Here, meteorological data measured at the position of the station are used as the input parameters of the model. In the third method, the required surface meteorological data are extracted from a NWP model. The World Research and Forecasting (WRF) model is used for this purpose. The WRF daily forecasts with a horizontal resolution of 0.1 degrees in 25 pressure levels (from 1000 to 50 mill bars) are used together with the GPS carrier phase and code measurements at station TKBN. This permanent GPS station is located at ϕ=36̊ 47ˊ 9.33˝ and λ=50̊ 55ˊ 48.20˝. Sampling rate of the GPS measurements is 30 seconds. The observation time interval starts at November 2 and ends at November 13, 2011.
Computed tropospheric corrections are applied to the raw measurements above. Daily precise positions of this station are estimated using the corrected data. Repeatability of the point positions is used as a measure for analyzing and comparing the obtained results. The repeatability of the station coordinates in the north component increases from 3.13 mm in the first method to 0.98 mm in the second one. The repeatability of the east and north components also improves by 1.73 mm and 2.11mm respectively when the raw observations are corrected by the tropospheric corrections which are derived from the second method above. This proves the efficiency the WRF numerical weather model for estimating the tropospheric error when the surface meteorological data that is observed at station is used.
The use of ray tracing with surface data from WRF model in comparison with Saastamoinen model does not lead to improved repeatability coordinates in all three components. The results of this study emphasize the Numerical Weather Prediction models used in this study has been poor to calculate the surface data. Otherwise the results of the ray tracing method based on WRF model with surface meteorological data is observed at station is the best method for Computed tropospheric corrections.

Since the launch of the first satellite to determine the global situation, more extensive use and the need for more accuracy is constantly increasing. Particularly one of the navigator concerns is the need to the continuous determination of status through using wireless network with a higher degree of confidence in urban areas. Unfortunately, the blind areas of the city are the opposite pole to precision in the positioning system because they are out of satellite's range of detection and reduce the precision of positioning. To overcome such problems, the use of pseudo-satellites as a complement to the GPS system can be effective in eliminating the weak signal reception and the ambiguity of the phase, and in achieving full accuracy and increasing the productivity of the Global Navigation Satellite System (GNSS).
Pseudo-satellites are ground-based devices that transmit waves that are similar to GPS waves, and can increase the efficiency of receivers as a complement. This tool is an additional observational factor to address the above mentioned disadvantages. But due to expenses and environmental problems, the number of pseudo-satellites that can be installed is limited. More importantly, to reduce the multi-path error of pseudo-satellite signals it is necessary to determine the position of the antenna and pseudo-satellite device with accuracy.
In general, this paper focuses on the development of a satellite simulator system and its proper economic appraisal to cover the blind spots of the GPS system in urban areas, using precise information of satellites and 3D maps.

Once man left his house for the first time in search of food, he needed a way to return home. The marking of rocks in the round trip, the use of coastline and celestial bodies such as the sun, the moon, and the stars were the first solutions that were less accurate and time-consuming. Later, with the development of technology of radio systems, and then the positioning satellites, routing became faster and more precise. The Global Positioning System was first created by the US Army in 1983, with an expense of $ 12 billion, and with launching the first satellite into space. This system has a variety of basic, manual and car models that have higher accuracy and cost, respectively. The system consists of three spatial, ground and user controls. The receivers will compare the time of sending the signal from the satellite with its receiving time and determine from the time difference the receiver's distance from the satellite. It is necessary to receive information from four satellites in order to find 3D coordinates. Availability in all hours of the day, any kind of weather conditions and ease of use are among the benefits of the system. Variants of this system include TRANSIT, GLONASS, SRARFIX and DORIS. System error sources include: user’s calculation errors and decrease in geometric accuracy.