基于混沌自适应烟花算法的雷达信号盲源分离

针对传统独立成分分析(independent component analysis, ICA)方法存在收敛速度慢、分离性能不高的问题,将混沌映射策略与自适应爆炸半径相结合,提出一种基于混沌自适应烟花算法(chaotic adaptive fireworks algorithm, CAFWA)的盲源分离(blind source separation, BSS)方法,并应用于雷达辐射源混合信号分选问题。混沌映射策略可以将初始值在解空间内分布更加均匀,爆炸半径能够根据适应度的优劣自适应改变,保证了所提算法局部搜索的精度,满足了全局搜索的多样性。实验结果表明所提算法可以在无噪和有噪情况下均能很好地分选观测信号,而且具有比传统算法更快的收敛速度和更优异的分选性能。

Blind source separation of radar signals based on chaotic adaptive firework algorithm

Aiming at the problem of slow speed on convergence and poor separation perfomance for the method of traditional independent component analysis (ICA). The strategy on chaotic maps strategy are combined with an adaptive explosion radius, and a kind of chaotic adaptive fireworks algorithm (CAFWA) based blind source separation (BSS) method is proposed, and applied into the sorting problem of radar emitter mixed signal. The chaotic maps strategy can distribute the initial value more evenly in the solution space, and the explosion radius can be changed adaptively according to the advantages and disadvantages of fitness, which ensures the precision of local search for the proposed algorithm and satisfies the diversity of universal search. The experimental results show that the proposed algorithm can sort observation signals well in both noise-free and noise conditions, and has faster constriction speed and better sorting function than the traditional algorithm.