A Particle-based Search Strategy for Improved Space Situational Awareness

In certain tracking applications, it is not sufficient to assume that the measurement of a target's state can be made whenever a sensor is tasked to do so. For example, the target's position may lie outside the sensor's limited field of view. Nevertheless, failure of this sort still yields some information. It tells us where the target is not. This information is difficult to capture in conventional filtering. In the context of catalogue maintenance of resident space objects, a central task in Space Situational Awareness, we demonstrate how the particle filter may be adapted to account for occasional failed observations and to guide the process of target reacquisition while maintaining a high quality track at other times. The results of a numerical simulation show that while an Unscented Kalman Filter can lose track of objects in more challenging circumstances, the proposed particle method consistently reacquires and tracks all objects.