| 余慧敏,习恺欣,刘曜玮,高月茹,杜保强.基于优化新模态分解的探地雷达信号去噪方法[J].电子测量与仪器学报,2026,40(4):100-109 |
| 基于优化新模态分解的探地雷达信号去噪方法 |
| Ground penetrating radar signal denoising method basedon optimized new mode decomposition |
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| DOI： |
| 中文关键词: 探地雷达 优化新模态分解 灰狼算法 改进小波阈值 去噪 |
| 英文关键词:ground penetrating radar optimization of ICEEMDAN GWO improved wavelet threshold denoising |
| 基金项目: |
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| Author | Institution |
| Yu Huimin | School of Information Science and Engineering, Hunan Normal University, Changsha 410006, China |
| Xi Kaixin | School of Information Science and Engineering, Hunan Normal University, Changsha 410006, China |
| Liu Yaowei | School of Information Science and Engineering, Hunan Normal University, Changsha 410006, China |
| Gao Yueru | School of Information Science and Engineering, Hunan Normal University, Changsha 410006, China |
| Du Baoqiang | School of Information Science and Engineering, Hunan Normal University, Changsha 410006, China |
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| 中文摘要: |
| 针对探地雷达信号在复杂的地下环境中容易受到噪声干扰、传统去噪方法存在模态混叠以及参数依赖性强的问题,提出了一种联合灰狼算法(GWO)、改进自适应噪声完备集合经验模态分解(ICEEMDAN)与改进的多参数控制小波阈值的去噪算法。首先采用GWO算法对ICEEMDAN中的噪声幅值(Nstd)和迭代次数(NE)进行寻优,实现参数的自适应调整并抑制模态混叠。然后设计改进的多参数控制小波阈值函数,对分解后的模态分量进行选择性的滤波重构,保留有效信号特征。仿真实验中,针对埋深为0.1 m的弱目标场景,该方法在输入信噪比为10 dB的条件下,输出信噪比提升至19.308 2 dB,比传统的自定参数的ICEEMDAN联合软硬阈值去噪算法平均提高了93.26%,波形相关系数为0.994 15,均方根误差为0.108 3;实测实验中,处理后的成像更清晰,目标的双曲线特征更加明显。该方法能够有效地提升探地雷达的信号质量,具有自适应性强、抗混叠和工程适用性高等特点,为地下目标的识别提供了可靠的技术支撑。 |
| 英文摘要: |
| Aiming at the problems that ground penetrating radar signals are easily interfered by noise in complex underground environments, traditional denoising methods have modal aliasing and strong parameter dependence, a denoising algorithm combining the grey wolf algorithm (GWO), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and improved multi-parameter controlled wavelet threshold is proposed. Firstly, the GWO algorithm is used to optimize the noise amplitude (Nstd) and the number of iterations (NE) in ICEEMDAN to achieve adaptive adjustment of parameters and suppress modal aliasing. Then, an improved multi-parameter controlled wavelet threshold function is designed to selectively filter and reconstruct the decomposed modal components to retain the effective signal characteristics. In the simulation experiment, for the weak target scene with a burial depth of 0.1 meters, under the condition of an input signal-to-noise ratio of 10 dB, the output signal-to-noise ratio of this method is improved to 19.308 2 dB, which is an average improvement of 93.26% over the traditional ICEEMDAN with custom parameters combined with soft and hard threshold denoising algorithm. The waveform correlation coefficient is 0.994 15 and the root mean square error is 0.108 3. In the actual measurement experiment, the processed image is clearer and the hyperbolic characteristics of the target are more obvious. This method can effectively improve the signal quality of ground penetrating radar, and has the characteristics of strong adaptability, anti-aliasing and high engineering applicability, providing reliable technical support for the identification of underground targets. |
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