A Novel Multi-satellite Tracking Algorithm for the LEO Satellite Communications

In the low earth orbit (LEO) satellite communication (SATCOM) scenario, the rough position of the satellite can be calculated from the ephemeris data. However, the multi-satellite tracking (MST) faces great challenges due to the inaccuracy of the ephemeris and interference of various sources. The traditional MST methods based on association suffer from high computational complexity, low estimation precision of satellite position and number. This paper then proposes an adaptive strong tracking unscented Kalman (ASTUK) MST algorithm based on probability hypothesis density (PHD), which reduces the computational complexity by setting the measurements partition strategy to optimally allocate the measurement data in the PHD filtering process, and improves the accuracy of state estimation by using the fading memory index (FMI) to adaptively update the information covariance of the gain matrix. Numerical simulations show the advantage of the proposed algorithm over the conventional ones on the computational complexity, tracking precision and estimation of satellite number.