  Tag population estimation has recently attracted significant research
attention due to its paramount importance on a variety of radio frequency
identification (RFID) applications. However, most, if not all, of existing
estimation mechanisms are proposed for the static case where tag population
remains constant during the estimation process, thus leaving the more
challenging dynamic case unaddressed, despite the fundamental importance of the
latter case on both theoretical analysis and practical application. In order to
bridge this gap, %based on \textit{dynamic framed-slotted ALOHA} (DFSA)
protocol, we devote this paper to designing a generic framework of stable and
accurate tag population estimation schemes based on Kalman filter for both
static and dynamic RFID systems. %The objective is to devise estimation schemes
and analyze the boundedness of estimation error. Technically, we first model
the dynamics of RFID systems as discrete stochastic processes and leverage the
techniques in extended Kalman filter (EKF) and cumulative sum control chart
(CUSUM) to estimate tag population for both static and dynamic systems. By
employing Lyapunov drift analysis, we mathematically characterise the
performance of the proposed framework in terms of estimation accuracy and
convergence speed by deriving the closed-form conditions on the design
parameters under which our scheme can stabilise around the real population size
with bounded relative estimation error that tends to zero with exponential
convergence rate.
