Open Access

ARTICLE

Modeling Impacts on Space Situational Awareness PHD Filter Tracking

C. Frueh¹

1 Purdue University, West-Lafayette, IN, USA.

Computer Modeling in Engineering & Sciences 2016, 111(2), 171-201. https://doi.org/10.3970/cmes.2016.111.171

Abstract

In recent years, probabilistic tracking methods have been becoming increasingly popular for solving the multi-target tracking problem in Space Situational Awareness (SSA). Bayesian frameworks have been used to describe the objects' of interest states and cardinality as point processes. The inputs of the Bayesian framework filters are a probabilistic description of the scene at hand, the probability of clutter during the observation, the probability of detection of the objects, the probability of object survival and birth rates, and in the state update, the measurement uncertainty and process noise for the propagation. However, in the filter derivation, the assumptions of Poisson distributions of the object prior and the clutter model are made. Extracting the first-order moments of the full Bayesian framework leads to a so-called Probability Hypothesis Density (PHD) filter. The first moment extraction of the PHD filter process is extremely sensitive to both the input parameters and the measurements. The specifics of the SSA problem and its probabilistic description are illustrated in this paper and compared to the assumptions that the PHD filter is based on. As an example, this paper shows the response of a Cardinality only PHD filter (only the number of objects is estimated, not their corresponding states) to different input parameterizations. The very simple Cardinality only PHD filter is chosen in order to clearly show the sole effects of the model mismatch that might be blurred with state estimation effects, such as non-linearity in the dynamical model, in a full PHD filter implementation. The simulated multi-target tracking scenario entails the observation of attitude stable and unstable geostationary objects.

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