摘要
随着噪声功率的增强,微弱目标信号的特征受噪声污染变得模糊且难以区分,导致微弱信号检测算法失效,提出一种可以保护目标信号特征的量子信号处理方法——局域半经典信号分析算法。详细介绍了算法实现量子化的原理和在量子域中保护目标信号特征的性质;给出算法步骤以及重要参数的计算方式;将所提算法与奇异值分解、小波阈值降噪算法结合进行了仿真分析和实验验证。结果表明,所提算法保护目标信号特征的能力可以帮助降噪算法检测极低信噪比的微弱信号,与其他方法结合可极大改善信噪比,准确提取信噪比为-30 dB的微弱目标信号,算法性能优越。
With the increase of noise power,the characteristics of weak target signal become blurred and difficult to distinguish by noise pollution,which leads to the failure of weak signal detection method.To solve this problem,a quantum signal processing method named Local Semi-Classical Signal Analysis(LSCSA)that could protect the target signal features was proposed.The quantization principle of the LSCSA and the properties of protecting target signal features in quantum domain were introduced.The implementation steps of the LSCSA and the calculation methods of important parameters were given.The LSCSA was combined with Singular Value Decomposition(SVD)and wavelet threshold de-noising method for simulation analysis and experimental verification.The verification results showed that the ability of the LSCSA to protect the features of target signals could help the denoising methods to detect weak signals with very low Signal-to-Noise Ratio(SNR).Combined with other methods,the SNR was greatly improved,and the weak target signals(SNR=-30dB)was accurately extracted.The performance of the LSCSA was superior.
作者
庾天翼
李舜酩
陆建涛
马会杰
龚思琪
YU Tianyi;LI Shunming;LU Jiantao;MA Huijie;GONG Siqi(College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;School of Automotive Engineering,Nantong Institute of Technology,Nantong 226002,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2024年第2期482-495,共14页
Computer Integrated Manufacturing Systems
基金
国家重点研发计划资助项目(2018YFB2003300)
国家自然科学基金资助项目(51975276)。
关键词
微弱信号检测
量子信号处理
保护特征
局域半经典信号分析
奇异值分解
小波阈值降噪
weak signal detection
quantum signal processing
protection of target signal features
local semi-classical signal analysis
singular value decomposition
wavelet threshold denoising