Probabilistic identification and discrimination of deep space objects via astrometric and photometric data fusion

In this dissertation two problems are studied in detail; shape estimation and space object classification. The progress made towards addressing these space situational awareness problems is discussed in this dissertation and simulation results are shown. The feasibility of the proposal approaches is shown. Additional areas of research are discussed, including generalized shape parameters estimation, inertia estimation, and mass estimation.;The main focus of this dissertation presents a new method, based on a multiple-model adaptive estimation approach, to determine the most probable attribute, such as shape, of a resident space object in orbit among a number of candidate attribute models while simultaneously recovering the observed resident space object's inertial orientation and trajectory. Multiple-model adaptive estimation uses a parallel bank of filters to provide multiple resident space object state estimates, where each filter is purposefully dependent on a mutually unique resident space object model. Estimates on the conditional probability of each model given the available measurements are provided from the multiple-model adaptive estimation approach. The multiple-model adaptive estimation state estimates are determined using a weighted sum of the individual models, weighted by model probabilities, whereas the shape estimates are determined from the model with the highest probability. Each filter employs the unscented estimation approach, reducing passively-collected electro-optical data to infer the unknown state vector comprised of the resident space object inertial-to-body orientation, position and respective temporal rates. Each hypothesized model results in a different observed optical cross-sectional area. The effect of solar radiation pressure may be recovered from accurate angles-data alone, if the collected measurements span a sufficiently long period of time so as to make the non-conservative mismodeling effects noticeable. However, for relatively short data arcs, this effect is weak and thus the temporal brightness of the resident space object can be used in conjunction with the angles data to exploit the fused sensitivity to both resident space object attributes and associated trajectory, the very same ones which drive the non-conservative dynamic effects. Recovering these attributes and trajectories with sufficient accuracy is shown in this dissertation, where the attributes are inherent in unique resident space object models. The performance of this strategy is demonstrated via simulated scenarios.