基于最佳匹配原则的自适应随机共振算法研究

Adaptive Stochastic Resonance Algorithm Based on Optimal Matching Principle

针对传统微弱信号检测手段在强噪声背景中改善探测性能不理想的问题,将随机共振应用到强噪声背景探测中,分析其基础理论,结合粒子群优化算法与最佳匹配原则,提出一种新的基于最佳匹配原则的自适应微弱周期信号检测方法。方法首先按最佳匹配原则估计随机共振系统参数,再通过优化算法在小范围内快速稳定地得出最佳系统参数与混合信号中微弱周期信号频率。实验结果表明,检测值与真实周期信号频率值误差仅为0.75%,并且方法提高了信噪比,实现了利用噪声增强微弱信号的目的。

It isn’t ideal to use the traditional weak signal detection methods to improve the detection performance in thestrong noise background.In this paper the stochastic resonance theory is applied to the signal detection in the strong noisebackground.The basic theory is analyzed,the particle swarm optimization algorithm is combined with the optimal matchingprinciple,and a new adaptive detection method for weak periodic signal based on optimal matching principle is proposed.Firstlyaccording to the optimal matching principle,the parameters of the stochastic resonance system are estimated,and then theoptimal system parameters and the weak periodic signal’s frequency in the mixed signal are obtained through the optimizationalgorithm in a small range.The experimental result shows that the error between the detected value and the true periodicsignal’s frequency is only0.75%,and the proposed method improves the SNR and realizes the purpose of using the noise toenhance the weak signal.