Sensor Management of Space-Based Multi-Platform EO/IR Sensors for Tracking Geosynchronous Satellites

摘要

We further develop our previous work on sensor management of disparate and dispersed sensors for tracking geosynchronous satellites presented last year at this conference by extending the approach to a network of Space Based Visible (SBV) type sensors on board LEO platforms. We demonstrate novel multisensor-multiobject algorithms which account for complex space conditions such as the phase angles and Earth occlusions. Phase angles are determined by the relative orientation of the sun, the SBV sensor, and the object, and play an important factor in determining the probability of detection for the objects. To optimally and simultaneously track multiple geosynchronous satellites, our tracking algorithms are based on the Probability Hypothesis Density (PHD) approximation of multiobject densities, its regularized particle filter implementations (regularized PHD-PF), and a sensor management objective function, the Posterior Expected Number of Objects.