Satellite coverage traffic volume prediction using a new surrogate model

Abstract

The space-based Automatic Dependent Surveillance-Broadcast (ADS-B) can significantly improve aircraft traffic efficiency and safety but suffers from the issue of signal collision. A potential solution for this limitation is employing an adaptive ADS-B antenna, e.g., the phased array antenna, which can adjust its coverage area in real-time during the satellite movement according to the satellite coverage traffic volume (SCTV). This paper proposes a new surrogate modeling technique, i.e., subregion radial basis function (RBF), and applies the proposed surrogate to predict the SCTV so that the adaptive strategy for the ADS-B antenna can be designed. Instead of training one model using all training samples as the classical RBF does, the proposed subregion RBF approach first partitions the input space into multiple fuzzy regions, then trains a RBF model in each region, and finally integrates the multiple subregion RBF models in a weighted average manner. By using this partition of unity strategy, the proposed method can greatly reduce the computational complexity of training models meanwhile maintain the interpolation property and high accuracy of RBF models. Further, the proposed subregion RBF is applied to construct a surrogate for the SCTV model to guide the design of the adaptive strategy for the space-based ADS-B. Simulation results show that compared with the classical and state-of-the-art methods, the proposed method has arrived a better balance between predictive accuracy and computational cost. In the simulation scenarios, it is also observed that by using the adaptive strategy, the detection probability of ADS-B receiver can always be greater than 95% and nearly 1/3 of power consumption can be saved. The simulations demonstrate the effectiveness of the proposed method.