# False Positive RFID Detection Using Classification Models
## 3 Dataset and Feature Extraction  
Figure 1 shows the structure employed to detect false positives using machine learning models. Data was collected for different tag movements and tag RSSs was recorded. Statistical features were then extracted for each tag, and outlier detection applied. After cleaning the data (i.e., removing the identified outliers), several machine learning algorithms were applied and compared. Several pallet movement scenarios were identified to investigate false positives. Figure 2a shows static and movement RFID tag examples that feasibly occur in actual warehouses. The RFID reader and antenna were installed in the warehouse exit gate before products were loaded in the vehicle. The main system purpose is to accurately record only those tags moved through the gate and subsequently loaded in the vehicle. However, the RFID reader could potentially read moved or static tags, where the moved tags could pass through the gate or close to the gate. This study considered true positive and false positive RFID readings. True positive readings were defined as the tags read when the pallets were moved through the gate and loaded onto the vehicle; whereas false positives were where pallets were moved close to the gate and the tags were read, but were not loaded onto the vehicle, or pallets were stationary within the reading range of the RFID reader. These accidental reads occur when the tags are located within the nominal read range or the range can be extended accidentally by metal objects within the field [21]. The purpose of the proposed classification model was to identify false positive tags based on extracted RSS information. Figure 2b shows a typical case where tagged products are moved through the gate. The tags read by the RFID reader through the antenna, and the tag data is forwarded to the client computer through wired connection. We developed the data gathering program to receive and present tag information as well as RSS data in real-time, as shown in Figure 2c. It took approximately 5 s to move the trolley through the gate, and the real-time tags and corresponding RSS details were presented on the screen (Figure 2c) and recorded during this gathering session. Figure 1: Radio frequency identification (RFID) readings based on classification models. We used 86 \(\times\) 54 \(\times\) 1.8 mm card type passive RFID tags for this trial, with the tags attached to the boxes and the trolley moved through the gate at different paths and speeds. The ultra high frequency (UHF) passive RFID tag model was 9662 with frequency 860-960 MHz. The IC type was Alien H3 while protocol was EPC Class1 Gen2 (ISO 18000-6C). The tag was packed with PVC material into a card-type passive tag. In addition, single reader ALR-9900+ from Alien Technology and linear antenna ALR-9610-AL with 5.90 dpi Gain were utilized in this experiment. The operating frequency of the reader was 902-928 MHz and supported EPC Class1 Gen2 (18000-6C). The ALR-9900+ is an enterprise reader, allowing users to monitor or read multiple tags as well as gather RSS data simultaneously at large distances. The Alien reader provides inventory command, a full-featured system for discerning the IDs of multiple tags in the field at the same time. In order to achieve good inventory performance, the reader can dynamically adjust the Q parameter to cause more or fewer tags to respond at any given time. The reader provides several modulation modes available with the Gen2 protocol, such as FM0, M4 (Miller 4), and the default RF mode value is 25M4 [46; 47]. To simplify the experiment process, we used default parameters provided by the reader. The reader provided a standard development kit incorporating several languages (Java, Net, Ruby), which allowed relatively simple integration into the developed data gathering program. Figure 2: _Cont._  
During the gathering session, different tags movement paths and speeds through the gate were implemented and performed, and the reading data labelled as true positive.