A separation algorithm for satellite-based AIS received signals based on SSA and RobustICA

In high-density traffic areas, ship collision automatic identification system (AIS) signals often occur, so high requirements are put forward for the separation performance and real-time performance of the receiver.

For mixed signals with different signal-to-noise ratios (SNR), a separation algorithm S-RICA based on singular spectrum analysis (SSA) and robust independent component analysis (RobustICA) is proposed. The Hankel matrix of the single-channel AIS signal is processed by singular value decomposition and reconstructed by time series respectively, SSA is used to replace whitening pre-processing in traditional independent component analysis (ICA), and the optimal step size of each iteration of the separation matrix is calculated using the kurtosis contrast function to quickly obtain the optimal separation matrix.

The simulation results show that the signal mean squared error (SMSE) value of S-RICA is stable at about 1.5 when the signal length changes, while the SMSE of fast independent component analysis (FastICA) is very unstable. S-RICA has a bit error rate of 0.97×10⁻² to 1.97×10⁻² at a SNR of 0 dB to 9 dB, an order of magnitude improvement over RobustICA and FastICA, and an improvement of 4 dB to 6 dB over S-FICA at an SNR of 0 dB to 7 dB. The average calculation time and number of iterations of S-RICA are about 18.5 ms and 13.6 times respectively, showing obvious advantages.

When the sample size and SNR change, S-RICA shows better separation performance. The results of this study have certain reference value for the application of S-RICA in future satellite-based AIS systems.