自适应步长学习指数星载AIS信号盲分离

Satellite-Based AIS Blind Source Separation Based on Adaptive Step Learning Index

针对传统等变自适应分解(EASI)算法解决星载AIS复信号盲分离收敛速度慢的问题,提出了一种自适应步长学习指数EASI算法。算法利用信号的非高斯性引出步长学习指数,使EASI算法的步长随步长学习指数的增大呈指数衰减。初始阶段步长较大加快了算法收敛,随着步长的减小,产生较小的稳态误差。对上述算法的批处理和自适应处理两种方式分别进行仿真,结果表明,改进算法较传统EASI算法收敛速度有所提高,且算法的自适应处理的稳态误差较传统EASI算法的自适应处理稳态误差也有所减小。

Aiming at the problem that the convergence rate of traditional equivariant adaptive source separation via independence（ EASI） algorithm is slow in solving blind source separation of satellite-based AIS complex signals,an EASI algorithm based on adaptive step learning index is proposed in the paper. The step learning index was derived from the non-Gaussian property of the output signal and the step size of EASI algorithm was exponentially decayed with the increase of the step learning index. In the initial stage,the larger step size accelerates the convergence rate of the algorithm,and the steady-state error decreases with the decrease of step size. The batch processing and adaptive processing of the above algorithm were simulated respectively. Experimental simulation results show that the convergence rate of the proposed algorithm is faster than that of traditional EASI algorithm,and the steady-state error of the adaptive processing of the proposed algorithm is less than that of adaptive processing of traditional EASI algorithm.