Dynamic PET reconstruction is a challenging issue due to the spatio-temporal nature and the complexity of the data. Conventional frame-by-frame approaches fail to explore the temporal information of dynamic PET data, and may lead to inaccurate results due to the low SNR of data. Due to the ill-conditioning of image reconstruction, proper prior knowledge should be incorporated to constrain the reconstruction. In this paper, we propose a tracer kinetics guided reconstruction framework for dynamic PET imaging. The dynamic reconstruction problem is formulated in a state-space representation, where compartment model serves as a continuous-time system equation to describe the tracer kinetic processes, and the imaging data is expressed as discrete sampling of the system states in a measurement equation. The reconstruction problem has therefore become a state estimation problem in a continuous-discrete hybrid paradigm, and sampled-data Hinfinity filtering is applied to for the estimation. As Hinfinity filtering makes no assumptions on the system and measurement statistics, robust reconstruction results can be obtained for dynamic PET imaging where the statistical properties of measurement data and system uncertainty are not available a priori.