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Optimization and Integration of RFID Navigation System by Using Different Location Algorithms

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The detection of mobile objects locations is vital for many fields of technology such as banking systems, social media, and driving assistance technologies (i.e. Google Maps). This approach is firstly proposed for supporting security and arm forces. Then, location-based services have gained the attention of data scientist so, it has become essential for providing access for particular files of service in accordance with geographical area/countries restrictions. So-to-say, location-based services have become paramount in modern days technologies. These systems are more likely required large knowledge of signal processing, radio frequency and electromagnetic theories. Several estimation algorithms are deployed in this paper for evaluating the location of mobile object over small scale indoor environment. In order to tackle connectivity problems such as No Line of Sight (LoS), energy consumption and time delay; Radio-frequency Identification (RFID) system is used to satisfy the requirements of real-life systems. In this paper, three location optimization algorithms namely Triangulation, Weighted Centroid Localization (WCL) and Bayes Decision Rule (with Kalman filter integration) have been used to evaluate the location of that mobile object. Every algorithm is examined with efforts in order the accuracy of each one to detect the location. However, the performance of all the algorithms is compared; the outcomes of Kalman filter integration with Bayes Decision rule algorithm have been more accurate.
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Location Estimation Algorithms; Triangulation; Weighted Centroid Localization; Kalman Filter; Bayes Decision Theorym; Radio Frequency Identification (RFID) Systems

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