## 2 Research Background
### RFID-Based Indoor Location Identification Methods  
To identify the location of an object using RFID, the following methods are regarded as the most popular: (1) a method using the arrival time of radio waves, (2) a method using the intensity of the signal, and (3) a method of recognizing the zone location. The first of these methods can be classified into time of arrival (TOA) and time difference of arrival (TDOA) [22]. TOA uses active tags and involves an algorithm that calculates the position of the tags using the arrival time of signals transmitted from three or more antennas [23]. TDOA also uses active tags and involves an algorithm that identifies the position of a tag by measuring the arrival time difference among the signals transmitted and received between two or more antennas and the tags. TOA and TDOA have accurate location recognition because they use the arrival time and time difference among the signals between the active tags and each antenna. However, precise time synchronization between the antenna and active tags is required to accurately identify the position of the tags. As a precise measuring device is required for this time synchronization, constructing a system for managing a warehouse where a plurality of item positions must be identified is expensive. For this reason, applying the TOA and TDOA algorithms for identifying the position of the multiple items in the warehouse is costly [24]. The main advantages of active RFID are that the tag has a built-in power source and can store its own location information and then transmit it to the RFID reader. Therefore, active RFID-based localization systems (LSs) have been researched in terms of various applications [25; 26; 27; 28; 29; 30]. The method involving the intensity of the signal is based on the principle that the intensity of the signal between the active tag and antenna becomes weaker as the distance between the tag and antenna increases. Washington University in the United States proposed a method known as \"SpotON\" to calculate the distance between the antenna and tag based on the intensity of the signal between the two apparatus [31]. LANDMARC [13] is the most common active RFID-based localization system, which uses the concept of a reference tag to determine the position of a tag using the distance between the former and the latter. There are several variations of LANDMARC such as [32] and [33]. [PERSON] et al. [32] applied the LANDMARC idea on regional localization instead of coordinates. [PERSON] et al. [33] showed how to reduce the computational overhead with a higher locating accuracy than LANDMARC. However, although these methods are simple to implement, the position information is inaccurate because the intensity of the signal can be easily changed due to the influence of obstacles within the vicinity. [PERSON] et al. [34] proposed the earliest passive RFID-based LS for the location sensing of mobile robots in indoor areas, with the robots equipped with passive RFID antennas and a laser scanner. Elsewhere, [PERSON] et al. [35] proposed a passive UHF (Ultra-high frequency) RFID-based indoor LS using a neural network and KNN (K-nearest neighbors) algorithms to predict the location of a tag of interest. Meanwhile, [PERSON] et al. [36] used a mobile RFID reader equipped with cameras with the image-recordable RFID reader service found to provide better location information for specific objects. With the use of a mobile camera for one targeted object, the possible physical location is narrowed down based on multiple images that record various times for the object. For their part, [PERSON] et al. [37] used a fixed RFID reader within a specific monitoring area. In a smart space, such as an office or home, a system, known as the Sherlock system, focuses on the localization of a human being who has a passive RFID tag. One of the features of this system is that the reader's antenna sweeps the monitoring area through various panning, tilting, and power-leveling movements to detect an object.