Discrimination of approaching multiple space targets is a challenging task for space situational awareness (SSA). Compared with ground-based radars, space-based radars cover better coverage and they have no restriction on the curvature of the earth. In this paper, we utilize space-based radar to track objects in space. Note that radar cross section (RCS) characteristics 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 poorly with the above distribution models under some altitude angles. To tackle this problem, we proposed a mixed log-normal (MLN) 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 multiple space-closed targets are misjudged as one under space targets 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 paper. Simulation results validate the effectiveness and robustness of the proposed RCS & Doppler information aided & GM weights improved probability hypothesis density (RDGI-PHD) filter.

The conventional two dimensional (2D) inverse synthetic aperture radar (ISAR) imaging fails to provide the targets' three dimensional (3D) information. In this paper, a 3D ISAR imaging method for the space target is proposed based on mutli-orbit observation data and an improved orthogonal matching pursuit (OMP) algorithm. Firstly, the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction dimension. Then, an improved OMP algorithm is applied to recover the space target's amplitude information via the 2D matrix data. Finally, scattering centers can be reconstructed with specific three dimensional locations. Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.

Space-based radars (SBR) systems are able to provide an unobstructed field of view for space target detection and tracking. However, the large temperature dynamic range and poor heat dissipation performance of the SBR system cause severe thermal noise, leading to deficiency in distant or dim space targets detection task. In essence, the challenges above can be categorized as typical low signal-to-noise ratio (SNR) problems and the track before detect (TBD) processing scheme is applied to solve them in this paper. Nevertheless, the typical TBD methods reckon without the following aspects, and thus are not well compatible with space target surveillance tasks via the SBR system. Firstly, the typical TBD methods discard the phase information of radar raw data in constructing the likelihood ratio. In addition, most existing work merely considers modeling the amplitude fluctuation as Swerling types, which is not accurate enough for space targets when compared with the log-normal distribution (LND) model. Moreover, orbital space targets follow the orbital dynamic principle while most existing TBD methods neglect this important information, which will cause space targets filtering estimation bias. To address the aforementioned problems, we propose a TBD method based on the complex-amplitude likelihood ratio (CLR) of the LND model and soft orbit-information constraint (OC). In this paper, with the aim of acquiring a more accurate likelihood ratio, we first derive the closed mathematical form of the amplitude likelihood ratio (ALR) and the CLR of the LND model. Meanwhile, some approximations are proposed to alleviate the integral computation. Then, the proposed ALR and CLR of the LND model are utilized to be implemented into the TBD scheme. Finally, we design elegant soft OC strategies to modify the associated weights corresponding with birth particles in sequential Monte Carlo (SMC) implementation. Simulation results are provided to validate the effectiveness of the proposed soft OC-CLR-TBD method.