首页|基于奇异谱和稳健独立分量分析的星载AIS接收信号分离算法

基于奇异谱和稳健独立分量分析的星载AIS接收信号分离算法

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  • 国家科技期刊平台
  • NETL
  • NSTL
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[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(SNR)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的独立成分分析(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(FastICA)算法则极不稳定;当SNR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SNR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SNR变化的情况下,S-RICA均表现出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.
A separation algorithm for satellite-based AIS received signals based on SSA and RobustICA
[Objective]In high-density traffic areas,ship collision automatic identification system(AIS)sig-nals often occur,so high requirements are put forward for the separation performance and real-time perform-ance 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 separa-tion matrix is calculated using the kurtosis contrast function to quickly obtain the optimal separation matrix.[Results]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(Fast-ICA)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.[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.

satellite communication systemssignal processingspectrum analysissatellite-based auto-matic identification systemfast independent component analysis(FastICA)robust independent component analysis(RobustICA)blind source separation

赵建森、谭智豪、段海燕、刘侠、王胜正

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上海海事大学 商船学院,上海 201306

卫星通信系统 信号处理 谱分析 星载自动识别系统 快速独立分量分析 稳健独立分量分析 盲源分离

2024

10.19693/j.issn.1673-3185.03464

中国舰船研究
中国舰船研究设计中心

中国舰船研究

CSTPCD北大核心
影响因子：0.496
ISSN：1673-3185
年,卷(期)：2024.19(6)