Cooperative space object tracking via universal Kalman consensus filter

Cooperative space object tracking using a sensor network plays an important role in space situational awareness and improves the accuracy, robustness, and dependability of space object tracking over a single sensor. However, different sensors may have different sampling rates and may work asynchronously. It is difficult to fuse these asynchronous measurements together and track space objects in time using traditional consensus-based filters, which require synchronous measurements. To overcome this restriction, a universal Kalman consensus filter (UKCF) is proposed. Based on the state transition matrix, each sensor can fuse the received information within a measurement period and track the object over time. Sensors with arbitrary sampling rates and working times can be incorporated into the cooperative tracking system. Optical observations are an efficient way to track space objects, especially medium- or high-orbit objects. The ground-based and space-based optical (SBO) tracking models are established first. Then, a centralized fusion algorithm for an asynchronous sensor network is deduced. Based on this algorithm, the topology for an asynchronous sensor network is established and the UKCF is produced. To demonstrate its performance, cooperative tracking scenarios for a geosynchronous object that use SBO sensors with different working times and use both SBO and ground-based optical sensors with different sampling rates are simulated. As shown in the paper, the UKCF, which has a similar performance relative to the KCF but expands its application range, is more suitable for real cooperative space object tracking.