Optimal and consistent estimation of the state of space objects is pivotal to surveillance and tracking applications. However, the performance of sequential probabilistic inference algorithms in space systems is restricted by non-Gaussianity and nonlinearity associated with orbital mechanics. In this paper, we present a UKF-PF based hybrid filtering framework for recursive Bayesian estimation of space objects. The proposed estimation scheme is designed to provide accurate and consistent estimates when measurements are sparse without incurring a large computational cost. It employs an unscented Kalman filter (UKF) for estimation when measurements are available. When the target is outside the field of view (FOV) of the sensor, the state probability density function (PDF) is updated via a sequential Monte Carlo method. The hybrid filter addresses the problem of particle depletion through a suitably designed transition scheme. Multiple variants of the hybrid filter are considered by modifying the PF-UKF transition. The hybrid filters are employed in three test cases in which a full three dimensional orbital motion model is considered by including the effects of J2 and atmospheric drag perturbations. It is demonstrated that the hybrid filters can furnish fast, accurate and consistent estimates outperforming standard UKF and particle filter (PF) implementations.