Effective methods and performance analysis on data transmission security with blind source separation in space-based AIS

In space-based Automatic Identification Systems (AIS), due to high satellite orbits, several Ad Hoc cells within the observation range of the satellite are vulnerable to interference by an external signal. To increase efficiency in target detection and improve system security, a blind source separation method is adopted for processing the conflicting signals received by satellites. Compared to traditional methods, we formulate the separation problem as a clustering problem. Since our algorithm is affected by the sparse-ness of source signals, to get satisfactory results, our algorithm assumes that the distance between two arbitrary mixed-signal vectors is less than the doubled sum of variances of distribution of the corresponding mixtures. Signal sparsity is overcome by computing the Short-Time Fourier Transform, and the mixed source signals are separated using the improved PSO clustering. We evaluated the performance and the robustness of the proposed network architecture by several simulations. The experimental results demonstrate the effectiveness of the proposed method in not only improving satellite signal receiving ability but also in enhancing space-based AIS security.