Study Of Indoor Location Algorithm Based On2.45GHz Active RFID Technology

In recent years, the need for orientation and navigation is increasing, with thedevelopment of the wireless technology and the mobile communication, especially thetechnology of internet of things and smart city. It is often necessary to determine the preciselocation of a person or a object in the room, especially in complex indoor environments,such as supermarket, hospital, museum, library, underground parking and so on. There aresome inferior positions that expensive, complex system and poor positioning accuracy withthe traditional indoor location technology, such as infrared ray and ultrasonic. Radiofrequency identification technology is becoming the first choice among the indoor locationtechnologies because of its low-cost, non-contact, non-line-of-sight, high precision.In this paper we studied the indoor location system based on2.45GHz active RFIDtechnology. Based on studying related technologies and positioning system, combined withthorough analyzing the indoor environment characteristics, comparing the currentcommonly used indoor positioning methods, We pointed out the characteristic of RFIDindoor location technology. The RFID indoor location technology can be usually dividedinto based on RSSI, TOA, TDOA, and AOA methods. We compare their advantages anddisadvantages, and focused on the design of the overall architecture, hardware and softwarebased on2.45GHz active RFID indoor location system.The experiment data is not simple applied directly to the location algorithm, Contrarily,the data is processed by noise filtering and data cleaning firstly, through the data processingmethods choice and filter design to ensure the availability of experimental data. This paperproposes two novel adaptive RFID location algorithms, they introduced feedbackmechanism of cybernetic and combined adaptive control strategy, fuzzy control strategyand location method together. This algorithm calculates the difference between real positionand location position of reference tag as feedback signal, and adjusts the path losscoefficient immediately. Then The signal model can automatically adapt to environmentalchanges. Without increasing system cost, we improve location accuracy and environmentaladaptability.