Objective Due to the unstable performance of traditional multi-channel automatic identification system (AIS) blind signal separation algorithms under low signal-to-noise ratio (SNR) conditions, and the fact that in actual communications, the number of source signals is often unknown, this paper proposes an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm based on preprocessing to enhance robustness and stability.

Method First, the received signal is preprocessed using the singular spectrum analysis (SSA) algorithm to reduce noise, utilizing the decomposition and reconstruction of the trajectory matrix of time series, we can effectively suppress Gaussian white noise and preserve key features of the signal. Next, the minimum description length (MDL) algorithm is used to estimate the number of source signals in the processed mixed matrix, by analyzing the eigenvalue distribution of the covariance matrix, we overcome the limitation of traditional algorithms that assume the number of source signals is known, and achieve adaptive identification of unknown source numbers. Finally, addressing the flaw of imprecise diagonalization measurement in the objective function of the traditional JADE algorithm, an improved JADE optimization algorithm is proposed to separate AIS mixed signals. It reconstructs an optimized objective function with scaling invariance and combines a column cyclic iteration strategy to enhance the accuracy of the joint diagonalization process while reducing computational redundancy.

Results By simulating the improved JADE algorithm, its performance was compared with that of the traditional JADE algorithm, the Fast Independent Component Analysis (FastICA) algorithm, the Robust Independent Component Analysis (RobustICA) algorithm, and the Information Maximization (Informax) algorithm. The results show that when separating 2, 3, and 4-channel AIS observation signals, the improved JADE algorithm achieves the highest correlation coefficient, indicating the strongest correlation between the separated signal and the source signal, and this value exceeds 0.801 5 across all SNR ranges from 0 to 20 dB. Additionally, in terms of crosstalk interference suppression, this method achieves the best suppression performance with values below 0.164 across all SNR ranges from -10 to 20 dB. Meanwhile, the algorithm's running time is reduced by 30.38% compared to traditional JADE, meeting the real-time requirements of spaceborne systems.

Conclusion The improved JADE optimization algorithm has significant advantages in terms of separation accuracy, algorithm reliability, and computational stability. The research results provide a theoretical basis and technical direction for improving the separation performance and real-time capabilities of AIS receivers and optimizing the design of maritime communication systems in actual engineering applications.