Passive Inter-Satellite Localization Accuracy Optimization in Low Earth Orbit Satellite Networks

Abstract

In this paper, a passive low earth orbit (LEO) satellite localization framework is investigated. In our considered model, one active satellite and multiple passive satellites are selected to localize a target LEO satellite, where the active satellite transmits signals to the target satellite and passive satellites receive signals reflected by the target satellite. Based on the received signals, passive satellites calculate the transmission distances and send this distance information to the active satellite that will estimate the position of target satellite. Since LEO satellites are powered by the sun, the available energy that can be used for target satellite localization is limited and dynamic. Hence, the satellite selection scheme must be optimized for improving the localization accuracy under the energy consumption constraints. This problem is cast into an optimization setting with a goal of minimizing target satellite positioning error by jointly optimizing active/passive satellite selection and transmit power allocation. To solve this problem, a mixture Gaussian distribution-based reinforcement learning (MGD-RL) method is proposed. The proposed MGD-RL method enables each LEO satellite to determine whether to be an active or a passive satellite and optimize its transmit power under the energy constraints. Furthermore, the proposed MGD-RL method can approximate the probability distribution of value functions by using mixture Gaussian distributions, thus reducing the training complexity of the designed RL. Simulation results demonstrate that, compared to a value decomposition network method and independent RL method, the MGD-RL method can improve the positioning accuracy of the target LEO satellite by up to 26.8% and 48.9%.