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In this paper, we present a UKF-PF based hybrid nonlinear filter for spaceobject tracking. Estimating the state and its associated uncertainty, alsoknown as filtering is paramount to the tracking process. The periodicity of theKeplerian orbits and the availability of accurate orbital perturbation modelspresent special advantages in filter design. The proposed nonlinear filteremploys an unscented Kalman filter (UKF) estimate the state of the system whilemeasurements are available. In the absence of measurements, the state pdf isupdated via a sequential Monte Carlo method. It is demonstrated that the hybridfilter offers fast and accurate performance regardless of orbital parametersused and the amount of uncertainty involved. The performance of the filterunder is found to depend upon the number of measurements recorded when theobject is within the field of view (FOV) of the sensors.

A UKF-PF based Hybrid Estimation Scheme for Space Object Tracking