## 3 Results
### Academic Literature and the Overall Development Trajectory of RFID
#### 3.3.5 Group 6: Localization Algorithms (Period: 2003-2019)  
The center path in Figure 5 consists of papers on the optimization of RFID localization algorithms. [PERSON] et al. [76] proposed a triangular RFID tag layout with which robot localization and orientation could be rapidly and accurately performed. [PERSON] and [PERSON] [77] presented a localization system established by placing RFID tags in a three-dimensional space; the system then defines the lines linking the tags on the ground as movement paths for the robot. Antennas can effectively read the relative localization errors within a given region. To reduce localization errors, [PERSON] and [PERSON] [78] proposed an algorithm that employs passive RFID. By using trigonometric functions and the Cartesian coordinates of UWB RFID tags, the algorithm can precisely estimate a robot's location and orientation. [PERSON] and [PERSON] [79] proposed an indoor positioning system that employs the Kalman filter to calculate the location of a reader. [PERSON] and [PERSON] [80] used the Kalman filter to combine RFID reader data with the mileage data of indoor autonomous vehicles; they achieved a mean localization error of approximately 10 cm. The same researchers also presented a system capable of achieving a mean localization error of 4 cm in which only two tags are used [64].  
[PERSON] et al. [13] observed how interactions between tags influence the k-nearest neighbor (k-NN) algorithm in calculating the radiation orientation of tag antennas and discovered that the revised k-NN algorithm achieves a small mean localization error. [PERSON] et al. [81] developed a localization measurement system comprising conventional UHF RFID tags and commercial RFID readers. The system makes accurate localization measurements in an 8.75-m\({}^{2}\) office, with the maximum estimation error being 0.032 m. According to the development trajectory indicated by the present analysis, the k-NN algorithm is the most popular algorithm used in this field. However, estimation of the k-value is a crucial problem. Additionally, because multiple methods are available for RSSI analysis, further improvement of the localization accuracy is difficult. [PERSON] et al. [82] proposed a novel similarity analysis-based indoor localization algorithm for indoor localization to optimize localization systems. [PERSON] et al. [83] established a new network planning model for determining the locations of a fixed number of readers; use of this model results in the minimal number of localization errors and amount of interference. [PERSON] et al. [84] proposed applying IKULDAS, an improved k-NN-based indoor localization algorithm, in localization and orientation systems to reduce the operation time and improve the precision of localization. [PERSON] et al. [85] employed an error concealment algorithm to improve the accuracy of location estimations. By using the location of adjacent tags, the algorithm estimates the error in the estimation; this approach achieves 26.5% higher estimation accuracy than benchmark estimators do.  
Figure 5: The development trajectory of academic papers on RFID research.  
In this study, the relationships between the six main clusters and the main path are illustrated in Figure 5. These themes were radio frequency identification applications in supply chain management, antenna design, collision prevention protocols, tag sensors, and localization systems in the main path. After using the specified branches analysis of keywords, it is possible to clearly understand the relationship between grouping and key-route main path. The results breakthrough that radio frequency identification applications in privacy and safety only appear on the key-route main path as shown in Figure 6.