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强噪声中检测微弱目标信号特征的量子信号处理算法

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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.
Quantum signal processing method for detecting weak target signal features in strong noise
With the increase of noise power,the characteristics of weak target signal become blurred and difficult to distin-guish by noise pollution,which leads to the failure of weak signal detection method.To solve this problem,a quantum sig-nal 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 pro-tect the features of target signals could help the denoising methods to detect weak signals with very low Signal-to-Noise Ra-tio(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.

weak signal detectionquantum signal processingprotection of target signal featureslocal semi-classical signal analysissingular value decompositionwavelet threshold denoising

庾天翼、李舜酩、陆建涛、马会杰、龚思琪

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南京航空航天大学能源与动力学院,江苏 南京 210016

南通理工学院汽车工程学院,江苏 南通 226002

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

国家重点研发计划资助项目国家自然科学基金资助项目

2018YFB200330051975276

2024

10.13196/j.cims.2021.0632

计算机集成制造系统
中国兵器工业集团第210研究所

计算机集成制造系统

CSTPCD北大核心
影响因子：1.092
ISSN：1006-5911
年,卷(期)：2024.30(2)
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