Adaptive PHD Filter With RCS and Doppler Feature for Space Targets Tracking via Space-Based Radar

Discrimination of approaching multiple space targets is a challenging task for space situational awareness (SSA). Compared with the ground-based radar, space-based radars cover a better coverage and it has no restriction of the curvature of the Earth. In this article, we utilize space-based radar to track objects in space. Note that radar cross section (RCS) characteristic and Doppler information are typical potential target individual features to improve the tracking performance for SSA tasks. However, one of the limitations of utilizing the RCS information is that the fluctuation of targets dynamic RCS is modeled by the typical ${{\chi }^2}$ distribution or Weibull distribution. In practice, fluctuating RCS of typical space targets, such as mission-focused space targets, fit poor with the above distribution models under some altitude angles. To tackle this problem, we proposed a mixed lognormal distribution model to compute the intensities of time-varying dynamic RCS likelihood. Then, the dynamic RCS likelihood and Doppler information are incorporated into the update recursion of the standard probability hypothesis density (PHD) filter. Furthermore, considering that some space-closed targets are misjudged as a one under some tracking scenarios, an adaptive threshold is devised to merge the Gaussian mixture (GM) components weights to ease this phenomenon simultaneously. The number estimation preservation proof is given and the GM implementation of the proposed filter is also provided in this article. Simulation results validate the effectiveness and robustness of the proposed RCS and Doppler information aided and GM weights improved PHD filter.