An adaptive wavelet threshold de-noising based on Mallat algorithm is applicated to moving target weak magnetic field signal detection in complex magnetic environment. The choice of threshold is the key problem. Based on the spectrum analysis of the magnetic field goal, an adaptive ARMA model threshold algorithm is adopted to improve the wavelet filtering performance. The simulation of this algorithm on measured data is carried out, and compared to Donoho threshold algorithm, which shows that adaptive ARMA model threshold algorithm significantly improved the capability of weak signal detection and extraction in complex magnetic environment.%探讨了基于Mallat算法的自适应小波阈值消噪在复杂磁环境下运动目标磁场弱信号检测方面的应用.阈值的选取是算法的关键,基于对目标磁场频谱分析,采用与尺度相关的自适应ARMA模型的阈值算法,改进在不同的小波分解层阈值滤波的性能.对实测数据进行算法仿真,并与Donoho阈值算法检测效果对比表明,自适应ARMA模型阈值滤波算法显著提高对目标磁场弱信号的检测和提取能力.
展开▼
