بهبود دقت تعيين موقعيت در شبكه بي سيم مبتني بر كشف الگو در محيط مسقف

Enhancing the accuracy of positioning process in Wi-Fi network based on pattern recognition in indoor area

با وجود گستردگي استفاده از سيستم تعيين موقعيت جهاني GPS، اين سيستم براي محيط هاي بسته و مسقف قابل استفاده نيست. روش هاي مختلفي براي توسعه ي سيستم تعيين موقعيت محيط هاي مسقف ارائه شده كه عموما بر اساس دريافت امواج راديويي ارسالي از فرستنده هايي با موقعيت مشخص هستند. زمان دريافت سيگنال، اختلاف زمان دريافت سيگنال، زاويه دريافت و اثر انگشت مكاني از جمله اين روش ها هستند. اما توجه به اين نكته ضروري است كه برخي از اين روش ها براي محيط داخل كه محيط پيچيده اي است، مناسب نيستند. روش هاي مبتني بر زمان دريافت سيگنال، اختلاف زمان دريافت سيگنال و زاويه دريافت سيگنال بر پايه ي تكنيك هاي مثلث بندي هستند كه نياز به ديد مستقيم فرستنده و گيرنده خواهد بود. همچنين سنجش دقيق زمان و زاويه سيگنال دريافتي نياز به ابزارهاي خاص دارند كه در بيشتر مواقع گران و پر هزينه هستند. در نهايت روش اثر انگشت مكاني مي تواند به عنوان روشي بهينه مورد استفاده قرار گيرد. روش اثر انگشت مكاني به علت عدم نياز به زيرساخت ويژه و امكان ايجاد ساده تر، به عنوان يك روش رايج مورد استفاده قرار مي گيرد. روش اثر انگشت مكاني براي تخمين موقعيت دستگاه همراه كاربر از توان سيگنال دريافتي استفاده مي كند. براي اين روش الگوريتم هاي مختلفي جهت كشف الگوي مكاني نقاط نمونه به كار برده مي شود كه از آنها به روش هاي احتمالاتي، روش نزديك ترين همسايگي و الگوريتم شبكه عصبي مصنوعي مي توان اشاره كرد. در اين مقاله اين سه روش با يكديگر مقايسه شده و در نهايت يك روش بهبود يافته نزديك ترين همسايگي ارائه شده است. با مقايسه روش پيشنهادي با ساير روش ها، برتري روش پيشنهادي تاييد مي شود.

Despite of widespread usage of Global Positioning System (GPS), this system is considered inefficient for indoor areas. Although the most prominent positioning system is Global Positioning System, this system uses some electromagnetic waves which are unable to pass thick obstacles such as concrete roofs and trees [1]. Thus, it cannot be considered as a robust infra-structure for indoor positioning purposes. Since, other signal networks like Wireless Local Area Network (WLAN) can be an appropriate alternative for indoor spaces. In addition, widespread usage of mobile smart instruments has provided the possibility of ubiquitous system’s development. Several methods have been proposed to obtain indoor positions which are generally based on received radio waves from fixed points. Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA) and Location fingerprinting can be used in this case. It is noteworthy that some of these methods are not really appropriate for indoor areas which maybe contain complex structure [2]. Time of Arrival, Time Difference of Arrival and Angle of Arrival methods use triangulation techniques so direct lines of sight are desired for them. And also acquisition of accurate time and angle of received signal without professional instruments, which are usually expensive, sounds almost impossible. Furthermore, for most of indoor areas such as commercial centers and museums direct line of sight is rarely available and signals are likely to be affected by multipath phenomena [3]. In recent years methods based on Inertial Measurement Units (IMU) have been proposed and programmed [4], [5]. These methods which are usually called Pedestrian Dead Reckoning (PDR) often employ sensors such as Gyroscope, Accelerometer and Magnetic sensors to obtain the position of the client [6]. It can be regarded as an important limitation along the objectives of the Ubiquitous systems. Such systems are restricted to clients equipped by platforms having these expensive modern sensors. Therefore, the methods using WLAN signals are usually preferred for location based services. WLAN Fingerprinting can be regarded as a most appropriate technique that uses signal strength as an identification parameter, which can be simply obtained. Furthermore, fingerprinting does not have any special infrastructure to establish and it can be conveniently laid out. In order to apply this method there are several ways to recognize the pattern of signals received from active transmitters. Stochastic method, Artificial Neural Network and K-Nearest Neighbor methods are some of classic pattern recognition techniques [7] that were investigated in this study. In this article these three methods were scrutinized and relatively compared, eventually an enhanced method has been offered. After using several data sets in order to assess the pattern recognition techniques, the proposed method got the first rank of the accuracy and also other techniques were ranked based on the accuracy. One of the most important differences between indoor positioning systems might be utilizing of various algorithms to recognize the spatial pattern. In this study, three popular classic methods including Probabilistic algorithm, Nearest Neighbor and Artificial Neural Network were investigated. The flowchart presented in Figure 1 has depicted the major steps of the study.