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Fuzzy processing on GPS data to improve positioning accuracy, before and after S/A is turned off
M. R. Mosavi
Department of Computer Engineering
Shahid Rajaee Teachers Training University
Lavizan, Tehran, Iran
M.Mosavi@srttu.edu
K. Mohammadi
Department of Electrical Engineering
Iran University of Science and Technology
Narmak, Tehran, Iran
Mohammadi@iust.ac.ir
M. H. Refan
Department of Electrical Engineering
Shahid Rajaee Teachers Training University
Lavizan, Tehran, Iran
Refan@srttu.edu
Abstract
This paper presents a method to determine an accurate position by using a low cost GPS receiver and proposes a fuzzy system for better accuracy in GPS positioning. At first the fixed position parameters, such as Geometric Dilution of Precision (GDOP) and Signal to Noise Ratio (SNR), are measured. Then those are applied to fuzzy system. Fuzzy system output defines as Reliable Factor (R.F.). Based on the R.F. values, the more accurate positions are selected. The steps design and implementation of the fuzzy system are presented and the experimental result of the tests are stated with real data, before and after Selective Availability (S/A) is turned off. Those show the errors of position components decrease due to using of the fuzzy system.
Introduction
Global Positioning System (GPS) has replaced prior positioning systems. It can cover all the earth by satellites to measure accurate time, altitude, longitude and latitude in every desirable point [1,2]. Positioning began from 1950s and improved in 1970s. In 1980s, GPS became an operational positioning system. At first it was designed and used for military purposes. But later its commercial applications have been increased. Nowadays commercial receivers take a great part in its market [1,2].
During past several years, the main problem to improve the positioning accuracy was S/A error, which was produced and fed into GPS system by U.S. Department of Defense (DoD) in order to degrade the achievable navigation accuracy when nonmilitary GPS receivers are used [3,4].
In addition to S/A, there is some other error sources that cause the position and time measuring from GPS receivers to be inaccurate. Other significant error sources are signal delays from ionospheric and tropospheric effects, satellite clock drift, satellite orbital position errors, signal multi path and noise generated within the receiver itself. Table1 shows the average errors introduced of GPS system in meter.
Table 1. The average errors introduced of GPS system in meter [5,6]
| Error Source |
Average (meters) |
Time Constant |
| Receiver Noise |
0.4 |
- |
| Troposphere |
0.5 |
More Than 1 Hour |
| Signal Multi Path |
0.6 |
0.5 to 10 Minute |
| Satellite Clocks |
1.5 |
- |
| Orbit Errors |
2.5 |
More Than 1 Hour |
| Ionosphere |
5 |
More Than 1 Hour |
| Selective Availability |
30 |
2 Minute |
Because of above mentioned error sources, all GPS receivers have a certain number of errors. This means that received data from GPS receiver will not reflect the real location. Therefore, users who wish to increase the accuracy of their GPS receiver must take steps to minimize the errors.
In this paper an intelligent method to decrease the positioning measurement errors in a low cost GPS receiver is described. The theoretical background for better accuracy is based on the principle of fuzzy logic.
GPS Receiver
To achieve information of position and implementing an operational system, MicroTracker Low Power (MLP) as a low cost GPS engine manufactured by Rockwell Company was used. This miniature receiver with a small volume is appropriate for a vast range of Original Equipment Manufacturer (OEM) products. OEM receiver provides the possibility of improving software by presenting raw data [7,8].
This receiver has 5 parallel channels. It can track up to 9 satellites simultaneously. This receiver supports approved and improved NMEA-0183 protocol. It can receive differential RTCM messages to improve the accuracy of positioning in differential mode. Its serial port can receive and transmit NMEA or Binary data with the rate of 4800 or 9600 bit per second. The Binary protocol provides more detailed information compare with NMEA protocol [7,8].
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