طراحی و پیاده سازی یک سیستم تشخیص محیط برای موقعیت یابی بدون مرز

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد سیستم اطلاعات مکانی، مهندسی نقشه برداری(ژئودزی و ژئوماتیک)، قطب فناوری اطلاعات مکانی، دانشگاه صنعتی خواجه نصیرالدین طوسی

2 دانشیار گروه GIS، مهندسی نقشه برداری، قطب فناوری اطلاعات مکانی، دانشگاه صنعتی خواجه نصیرالدین طوسی

10.22131/sepehr.2019.34613

چکیده

امروزه خدمات مکان- مبنا یکی از برنامههای پرکاربرد در ابزار همراه است؛ اما دو مانع وجود دارد که خدمات مکان- مبنا نتواند از تمام پتانسیل خود استفاده کند. اول اینکه موقعیت مکانی افراد در تمام نقاط قابلدسترس نیست. مانع دوم عدم وجود سرویس یکپارچه برای تعیین موقعیت میباشد. مانع دوم که همان عدم وجود سرویس یکپارچه برای تعیین موقعیت است در این تحقیق موردبررسی قرارگرفته است. کاربران تمایل زیادی دارند که بتوانند با دستگاه تلفن همراهی که در اختیاردارند در محیطهای داخلی و بیرونی بهصورت یکپارچه تعیین موقعیت کنند. برای تعیین موقعیت در محیطهای داخلی و خارجی با توجه به ویژگیهای آنها روشهای متفاوتی وجود دارد. پس بنابراین برای استفاده از روش مناسب تعیین موقعیت در هر محیط ابتدا نیاز به شناخت محیط وجود دارد.
در راستای تشخیص محیط از چهار عامل نور، تعداد ماهواره، دقت و امواج شبکه بیسیم و برای انجام تصمیمگیری از سیستم خبره استفاده شده است. در ابتدا چهارده حالت مختلف استفاده از عوامل تشخیص محیط موردبررسی قرار گرفت. سپس برای ایجاد یک سیستم خبره کلاسیک با اندازهگیریهای انجامشده حدود آستانه هر یک از عوامل تعیین شد. پس از آن، برای حالتهای مختلف، قوانین لازم برای تشخیص محیط تعریفشده و پایگاه دانش تشکیل شد. درنهایت بعد از تشکیل پایگاه داده در نقاط مختلف منطقه موردمطالعه این سیستم ارزیابی و نتایج آن ارائه شده است. نتایج این روش نشان میدهد در حالتی که از نور، دقت و امواج شبکه بیسیم استفاده شده، بهترین نتیجه بهدستآمده است. در این حالت در 92 درصد موارد تشخیص محیط بهدرستی صورت گرفته است.

کلیدواژه‌ها


عنوان مقاله [English]

Design and implementation of an environment detection system for positioning without border

نویسندگان [English]

  • Mohsen Mehrabi nejad 1
  • Mohammadreza Malek 2
1 M. Sc. Degree in civil-surveying engineering in GIS, Khaje Nasir Toosi University of Technology
2 Associate professor in GIS department, Khaje Nasir Toosi University of Technology
چکیده [English]

 
Extended Abstract
Introduction
Initially, researches were conducted on the positioning inside and outside of the building separately, and separate methods have been presented to determine the position inside and outside the building. But today, they are seeking methodswithout borderfor the positioning that can be used to conduct both indoor and outdoor location.  Today, location-based services are one of the most widely used programs in mobile tools, but there are two obstacles that prevent these services from using their full potential. First, the location of individuals is not accessible at all points. The second obstacle is the lack of an integrated service to determine the position. The second obstacle has been addressed in this research. Users tend much to do the integrated positioning in internal and external environmentsusing their mobilephone devices. There are various methods for positioning in interior and exterior environments with respect to their characteristics. Therefore, in order to use the appropriate method for positioning in any environment, first there is a need to know the environment. 
Materials & Methods
In this research, the definiteexpert system was used for the detection of the environment. In the definite state, the existing space is divided into two internal and external parts. The internal space is the internal parts of the building and the rest of the spaces are known as the external space. First we specify the various factors in the detection of the environment, and then, to establish the knowledge base by definite method, different modes of the combination of factors were taken into consideration and finally, the results of each method were investigated. In order to detect the environment, four factors of light, numbers of satellites, accuracy and waves of wireless network were used. The expert system was used for decision-making.Regarding the number of satellites, it can be said that since receiving the waves of satellites in internal environments is difficult and the communication of the receiver with a number or all of the satellites is interrupted, therefore, it is expected that the number of satellites whose waves are receivable inside the building to be less than the satellites which are located on the horizon of that region.Another one of the factors of environment detection, the satellite signals are received by the receiver and the positioning is done. Due to the presence of the building and obstacles in these areas, the positioning is done with low accuracy, therefore, given that the accuracy of positioning in external environment is higher than that in internal environment, the detection of environment can be carried out with respect to the measurement of the positioning accuracy.  One of the factors that indicate the accuracy of positioning is the DOP value. The accuracy of the two-dimensional positioning is determined by HDOP which indicates the accuracy of planimetric positioning. Given that the accuracy of positioning using GPS in the external environment is better than the internal positioning accuracy, therefore, the HDOP values in the internal environment will be higher than external environment.
The power of received waves of wireless networks depends on the receiver’s distance from the transmitter. The more the receiver is close to the transmitter, the greater will be the power of the received waves. Since the wave transmitters of the wireless networks are usually installed inside the building, therefore, the receiving power of these waves inside the building will be higher than the outside. Therefore, it is possible to perform the detection of the environment by measuring the receiving power of these waves. 
Researches show that the amount of light for indoor environment is generally much lower indoor than open or semi-openspaces, even in cloudy and rainy days. This is true even when the light sensor is rotated downwards.  Its main reason is that the intensity of the sunlight is in the visible spectrum which is usually much higher than lamp light. Moreover, the light sensor has the ability to detect the light in the invisible spectrum like infrared and ultraviolet. When the sunlight and artificial light appear to be the same, the bright flux of the sunlight is much higher than the artificial light sourcesthroughout the day, therefore, the indoor environment of the building can be different from the outdoor in terms of the light intensity. This research was implemented in the mapping department of KhajehNasir University. The outer area of this university was considered as the outdoor environment and the corridor of the mapping department building’s ground was studied as the internal environment.
 
Results & Discussion
To create a classical expert system, the thresholds of each of the factors were determined by the measurements done. Then, for different modes, the necessary rules for detecting the environment were defined and the knowledge base was formed. Finally, after the formation of the database in different parts of the study area, the system was evaluated and its results were presented. We divided the study area into two internal and external environments.To evaluatethe results, we were deployed in each of the aforementioned places4 times, and we matched the environmentdetectionby the system with the reality and evaluated the amount of system accuracy.
Conclusion
The results of this method show that the best result has been obtained when the light, accuracyandthe waves of wirelessnetwork were used. 92% of the detection of environment has been carried out correctly. The results also showed that in some exterior area, the detection of environmentwas associated with mistakesdue to the presenceof false ceilings and insufficient light.  In the case of the accuracy and the number of satellites, in the external areas where there were high walls and satellite waves were not well received, the detection of environment was not done correctly.

کلیدواژه‌ها [English]

  • Location-based service
  • Environment detection
  • Positioning without border
  • Mobile positioning
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