## 5 Design and Implementation of Sing-Ant  
The main goal of the proposed system is to allow the accurate prediction of the location by using a combination of passive RFID reference tags via the LANDMARC approach and also use multiple antennas to better perceive the depth and different orientations of distance from the center. Therefore, this proposed system would allow for more accurate localization as it utilizes antenna positioning and also reference tags in order to narrow down the precise location of the target tag. The details of our proposed method are as follows. From Equations (1) and (2), we can obtain multiple sub-matrices of the Euclidian distance between the target tag and the total number of reference tags, which are all mapped to the corresponding antenna that is being read. In order to make meaningful sense of the data, we split them into three calculations in order to best determine the mathematical model used to calculate the outcome. The three main calculation models are core, sector and cluster. Since each of these calculations uses different configurations individually, which might be unable to accurately predict the location of the target tag, this can narrow down the location by focusing on their major advantages and lessening the disadvantages of the other systems. The core is ideal when tags are displaced near the center of the effective area of operation. Thus, when calculating the location of the target tag near the center, the first task is to create a reference list of all the tags that need to be scanned and create a custom RFID tag filter in order to limit reads, and hence devote more resources to the target tags and also allow higher read event success by limiting unwanted cards. This can be easily implemented by the use of a HashMap filter. The block diagram for filtering out unwanted tags is shown in Figure 4.  
After tag filtering is finished, the next step is to choose the best values per antenna beam in order to select a single value to represent the RSSI. Because the RSSI is a fluctuating value that is highly inconsistent, in one antenna activation cycle, if the distance is close enough, we can obtain an average of 15 successful tag reads. The values, however, are sinusoidal in nature, and hence multiple mathematical modeling techniques can be used to filter the EPC values obtained, and the values are smoothed to be considered for calculation. Figure 5 shows the oscillating values of EPC. One of the easiest and most primitive methods to stabilize the results is to consider only those above or below the minimum and maximum  
Figure 3: All antenna sectors (**left**) and a single antenna sector (**right**). average. This forces the final results to either be floor results or ceiling results. Throughout this test, the results are used. If only one EPC RSSI value is needed, then the maximum RSSI value per antenna beam cycle can be chosen. In Figure 5, the two diagrams show how we can parse the received RSSI values in order to accommodate and allow for steadier and linear results; by choosing either the floor or the ceiling threshold, we can obtain fewer conflicting data. During the calculation, we deferred to the ceiling values as they showed the highest possible RSSI per RFID tag. In an ideal situation, lower RSSI values would arise from tags at a further distance. However, in the practical test, as a result of the complex environmental factors and multipath effect, the RSSI value from the device could not directly reflect the distance between the tag and the antenna [16]. Some RSSIs of reference tags and tags to be tested can be grouped into theoretical values and measured values according to different formulas [18]. The results can be logged to allow for further study, such as for deep learning models of estimation. The main idea behind position estimation via mapping techniques is to determine a regression scheme based on a set of reference tags' data, and then to estimate the position of a given node according to this regression function [19].