# False Positive RFID Detection Using Classification Models
## 2 Related Work  
### False Positive Detection  
RFID technology can be utilized for WMSs since it can detect any object with an attached tag located within the reading range. Although RFID readers can be installed at the exit gate to record products loaded onto trucks to be moved to other supply chain partners, the reader cannot distinguish between tags that move through the gate and those that accidentally appear in the reading range, but were not subsequently loaded. Several methods have been proposed to detect RFID false positives, including sliding window, extra hardware, RSS, phase, and doppler-based methods. Sliding windows methods rely on tag occurrence and timestamp. [PERSON] et al. (2006) proposed a sliding window approach to remove noise (false positives) and duplicates from continuous high-volume RFID data streams [34]. The proposed model considered fixed sized windows that moved with time (i.e., sliding windows) and a threshold. The model scanned all windows and removed RFID tags where the occurrence was below the defined threshold. However, this approach requires the window size and threshold to be accurately defined prior to analysis for optimal results. [PERSON] et al. (2006) determined optimal window size using Statistical sMoothing for Unreliable RFid data (SMURF), an adaptive smoothing filter, to clean false positives [35]. SMURF determines optimal window size automatically based on the underlying data stream characteristics. Their results showed that SMURFtechniques could adjust window size and outperformed all static window schemes. [PERSON] et al. (2012) proposed a window sub-range transition detection (WSTD) adaptive sliding window approach, which used binomial sampling to calculate the appropriate window size [36]. Their results show that the WSTD scheme outperformed SMURF. In addition, the tag occurrence and timestamp can be utilized for indoor RFID-tracking system. Offline cleaning technique (grid-based filtering and sampling-based model) for addressing missing detections [37] and probabilistic framework for cleaning trajectories [38] have been presented in RFID-tracked moving objects. The results showed that the proposed model outperformed other models and provided remarkable cleaning efficacy for different scenarios. Finally, [PERSON] et al. (2016) proposed a learning-based approach to solve the false negatives (miss-reads) and false positives (cross readings) problems [39]. The hidden Markov model was built to be used as a building block for the cleaning process of time-series raw RFID data. The experimental results have showed the efficiency and effectiveness of the proposed model compared to other models. Extra hardware methods rely on additional hardware (e.g., reader(s) and/or antenna(s)). [PERSON] and [PERSON] (2011) proposed decision support models utilizing extra readers and tags to determine false positives [40]. If both readers recognized the majority of the reads, then the tagged object was considered to be present, whereas false positives were confirmed if the tag was not read by any readers. If only one reader detected the tagged object, then several decision rules were triggered to finally decide whether the tagged object was present or not. Their results showed that the proposed decision support model successfully improved false positive detection. [PERSON] et al. (2012) showed that adding additional antennas improved false positive RFID tag detection [41]. The study proposed several portal antenna setups, including standard, satellite, and transition portals. Standard portals utilized a single reader with four different antennas, whereas satellite and transition portals used an additional RFID reader with four more antennas. Although the same number of readers and antennas were defined between satellite and transition portals, the antenna setup differed significantly. Combined with the classification algorithm, adding more readers and antennas provided higher RFID classification accuracy for satellite and transition portals compared to the standard portal.