基于级联随机共振与APSO算法相结合的信号检测方法

Signal Detection Method Based On Cascaded Stochastic Resonance Combining With APSO Algorithm

针对随机共振方法以系统的参数和噪声强度的匹配为研究背景的局限性,为解决级联双稳系统参数的合理选取的问题及克服自适应随机共振单参数优化的不足之处,提出了一种基于级联随机共振与自适应粒子群(APSO)算法相结合的方法。该方法以系统的输出信噪比为优化目标函数,采用自适应粒子群算法较强的全局搜索能力和粒子(待优化参数)的多样性,对级联双稳态随机共振的级联系统参数进行同步优化,使系统处于最佳随机共振工作状态。仿真结果表明,该方法可以提高输出信噪比及算法的收敛速度,实现良好的检测效果。

Combining stochastic resonance and adaptive particle swarm optimization（APSO） was presented to overcome the limi- tation of stochastic resonance under matching of the system parameters and noise intensity background. The shortcomings of the con- ventional function, such as reasonable selection of cascaded bistable system （CBS） parameters and a single parameter optimization in traditional adaptive stochastic resonance, can be solved. Taking the system output signal-to-noise ratio （SNR） as the optimizing objec- tive function ,and conducting to the multi-parameter synchronous optimization in the CBS by using the exploratory capability of the algorithm and the diversity of the Pareto solutions which keep the stochastic resonance in an optimal state. Experimental results show that this method can raise SNR and convergence speed quickly, effectively, and can achieve good detection results.