赵建森, 谭智豪, 段海燕, 等. 基于奇异谱和鲁棒性独立成分分析的星载AIS接收信号分离算法[J]. 中国舰船研究, 2024, 19(X): 1–10. doi: 10.19693/j.issn.1673-3185.03464
引用本文: 赵建森, 谭智豪, 段海燕, 等. 基于奇异谱和鲁棒性独立成分分析的星载AIS接收信号分离算法[J]. 中国舰船研究, 2024, 19(X): 1–10. doi: 10.19693/j.issn.1673-3185.03464
shu
ZHAO J S, TAN Z H, DUAN H Y, et al. A separation algorithm for satellite-based AIS received signals based on SSA and RobustICA[J]. Chinese Journal of Ship Research, 2024, 19(X): 1–10 (in Chinese). doi: 10.19693/j.issn.1673-3185.03464
Citation: ZHAO J S, TAN Z H, DUAN H Y, et al. A separation algorithm for satellite-based AIS received signals based on SSA and RobustICA[J]. Chinese Journal of Ship Research, 2024, 19(X): 1–10 (in Chinese). doi: 10.19693/j.issn.1673-3185.03464
shu

基于奇异谱和鲁棒性独立成分分析的星载AIS接收信号分离算法

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

    摘要
  • 摘要:
    目的 在高密度流量地区，船舶经常出现自动识别系统（AIS）信号碰撞的问题，故对接收机的分离性能和实时性能均提出了很高的要求。
    方法 针对不同信噪比下的混合信号，提出一种基于奇异谱分析（SSA）与稳健独立分量分析（RobustICA）的分离算法S-RICA。通过对单通道AIS信号的Hankel矩阵分别开展奇异值分解和时间序列重构，并利用奇异谱分析代替传统独立成分分析（ICA）中的白化预处理，再采用峰度对比函数来计算分离矩阵每次迭代的最优步长，从而快速获取最优分离矩阵。
    结果 仿真实验结果表明：当信号长度改变时，S-RICA的信号均方误差均可稳定在1.5左右，而快速独立分量分析算法（FastICA）则极不稳定；当信噪比为0～9 dB时，S-RICA的误码率为0.97×10−2～1.97×10−2，其性能较RobustICA和FastICA提升了1个数量级，且其在信噪比为0 ～ 7 dB时比S-FICA提高了4 ～ 6 dB；S-RICA的平均计算时间和迭代次数分别为18.5 ms和13.6次左右，具有明显的优势。
    结论 在样本容量和信噪比变化的情况下，S-RICA均表现了更为优异的分离性能，研究成果可为S-RICA在未来星载AIS系统中工程应用提供参考。

     

    Abstract:
    Objectives 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.
    Methods 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.
    Results The simulation results show that the 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 0.97×10−2–1.97×10−2 at a signal-to-noise ratio (SNR) of 0–9 dB, an order of magnitude improvement over RobustICA and FastICA, and an improvement of 4–6 dB over S-FICA at an SNR of 0–7 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.
    Conclusions 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.

     

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