In a radio frequency identification （RFID） system, the backscattered signal is small and prone to interference. The performance of RFID tag identification in interference scenarios is degraded compared to that in er...In a radio frequency identification （RFID） system, the backscattered signal is small and prone to interference. The performance of RFID tag identification in interference scenarios is degraded compared to that in error-free scenarios. In this paper, a novel Mahalanobis distance estimate （MDE） method is proposed to jointly estimate the number of tags and packet error rate （PER）. The MDE method is error resilient owing to its ability to achieve a stable estimation when interference is impairing the observed information. The proposed method achieves significantly enhanced accuracy over existing methods by taking all the information and correlations among the observed results into account. The MDE method improves the estimate performance based on efficient decorrelation and classification of the observed information. Moreover, the performance of the PER estimate is analyzed both in theory and through simulations. It can be concluded from the analysis that the estimated PER is unbiased and variance-bounded. Simulations show that the proposed estimate outperforms the previous proposals in terms of accuracy and stability, which makes it suitable for application in interference scenarios.展开更多
基金supported by the National Basic Research Program of China (2011CB302900)
文摘In a radio frequency identification （RFID） system, the backscattered signal is small and prone to interference. The performance of RFID tag identification in interference scenarios is degraded compared to that in error-free scenarios. In this paper, a novel Mahalanobis distance estimate （MDE） method is proposed to jointly estimate the number of tags and packet error rate （PER）. The MDE method is error resilient owing to its ability to achieve a stable estimation when interference is impairing the observed information. The proposed method achieves significantly enhanced accuracy over existing methods by taking all the information and correlations among the observed results into account. The MDE method improves the estimate performance based on efficient decorrelation and classification of the observed information. Moreover, the performance of the PER estimate is analyzed both in theory and through simulations. It can be concluded from the analysis that the estimated PER is unbiased and variance-bounded. Simulations show that the proposed estimate outperforms the previous proposals in terms of accuracy and stability, which makes it suitable for application in interference scenarios.
