基于均值二分的改进型FCME算法及其在极/超低频信道噪声检测中的应用

Improved FCME Algorithm Based on Binary Searching by Mean and Its Applications in E/SLF Channel Noise Detection

极/超低频信道噪声脉冲因接收机前端暂态效应而钝化,导致常规时域幅度门限检测器的性能出现退化。针对该问题,文中提出了一种基于局部方差域变换(Local Variance Domain Transforming,LVDT)恒虚警率顺序统计分析(OS-CFAR)的检测算法。同时,针对FCME(Forward Consecutive Mean Excision)算法在迭代计算背景噪声时可能存在的发散问题,提出了一种基于均值二分搜索(Binary Searching Method by Mean,BSMM)的改进方法,BSMM无需初始集假设以及排序过程,因而具有更好的鲁棒性和更高的计算效率。仿真结果表明,与常规FCME算法相比,在不损失背景噪声估计精度的条件下,所提BSMM的计算时间平均缩短2个数量级以上,所提信道噪声检测算法优于局部最优非线性检测算法。

Extreme/Super Low Frequency(E/SLF,3~300 Hz)channel noise(CN)impulses are usually passivated by the transient effects in the receivers’front-end stages,and it will cause the performance degradation for the common time-domain amplitude-based threshold detectors.Aiming at this problem,this paper proposed a detection method based on the constant false alarm rate ordering statistics(OS-CFAR)through local variance domain transforming(LVDT).In light of the potential divergency problem when FCME algorithm iteratively evaluates the background noise,this paper also presented an improved method namely binary searching method by mean(BSMM).BSMM doesn’t need to assume the initial clean set or sort process,and thus is more robust and has higher efficiency.Simulations show that the proposed method can reduce the computing time by more than 2 orders without losing estimation accuracy of background noise compared with the common FCME.Besides,the proposed CN detection method outperforms the local optimum threshold nonlinearities method(LOTNI).