The rapid development of Low Earth Orbit (LEO) mega constellations has significantly contributed to several aspects of human scientific progress, such as communication, navigation, and remote sensing. However, unrestrained deployment of constellations has also strained orbital resources and increased spacecraft congestion in LEO, which seriously affects the safety of in-orbit operations of many space assets. For the long-term and sustainable development of space activities in LEO regions, space environment stability must be maintained using more rational surveillance and governance mechanisms. This review contributes to the research gap and facilitates the development of LEO mega constellations. First, the current development of typical LEO mega constellations is reviewed, followed by the analysis of the impact of LEO mega constellations in terms of astronomical observation, spacecraft safety in orbit, and space environment evolution. Then, two main solutions to conduct the challenges raised by LEO mage constellations are elaborated: one is to ensure the safety operation of spacecraft using space surveillance infrastructures and space situational awareness technologies, and the other is to accelerate the deorbit of constellation satellites at the end of life based on postmission disposal and active removal methods. Finally, the future development and potential research directions of LEO mega constellations are prospected.

This paper presents a flexible modeling framework for multi-target tracking based on the theory of Outer Probability Measures (OPMs). The notion of labeled uncertain finite set is introduced and utilized as the basis to derive a possibilistic analog of the $\delta$-Generalized Labeled Multi-Bernoulli ($\delta$-GLMB) filter, in which the uncertainty in the multi-target system is represented by possibility functions instead of probability distributions. The proposed method inherits the capability of the standard probabilistic $\delta$-GLMB filter to yield joint state, number, and trajectory estimates of multiple appearing and disappearing targets. Beyond that, it is capable to account for epistemic uncertainty due to ignorance or partial knowledge regarding the multi-target system, e.g., the absence of complete information on dynamical model parameters (e.g., probability of detection, birth) and initial number and state of newborn targets. The features of the developed filter are demonstrated using two simulated scenarios.