From Static to Dynamic Tag Population Estimation: An Extended Kalman Filter Perspective

TL;DR

This paper introduces a Kalman filter-based framework for accurate tag population estimation in RFID systems, addressing both static and dynamic scenarios with proven stability and convergence properties.

Contribution

It extends existing static estimation methods to dynamic RFID systems using an extended Kalman filter, providing a stable, accurate, and theoretically grounded estimation scheme.

Findings

The proposed method stabilizes around the true tag population with bounded error.

The estimation error converges exponentially to zero under certain conditions.

The framework is applicable to both static and dynamic RFID environments.

Abstract

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.