Lopac Nikola, Lerga Jonatan, Cuoco Elena
Faculty of Maritime Studies, University of Rijeka, Studentska 2, 51000 Rijeka, Croatia.
Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Radmile Matejcic 2, 51000 Rijeka, Croatia.
Sensors (Basel). 2020 Dec 3;20(23):6920. doi: 10.3390/s20236920.
Gravitational-wave data (discovered first in 2015 by the Advanced LIGO interferometers and awarded by the Nobel Prize in 2017) are characterized by non-Gaussian and non-stationary noise. The ever-increasing amount of acquired data requires the development of efficient denoising algorithms that will enable the detection of gravitational-wave events embedded in low signal-to-noise-ratio (SNR) environments. In this paper, an algorithm based on the local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule for the filter support selection is proposed to denoise the gravitational-wave burst signals from core collapse supernovae. The LPA-RICI denoising method's performance is tested on three different burst signals, numerically generated and injected into the real-life noise data collected by the Advanced LIGO detector. The analysis of the experimental results obtained by several case studies (conducted at different signal source distances corresponding to the different SNR values) indicates that the LPA-RICI method efficiently removes the noise and simultaneously preserves the morphology of the gravitational-wave burst signals. The technique offers reliable denoising performance even at the very low SNR values. Moreover, the analysis shows that the LPA-RICI method outperforms the approach combining LPA and the original intersection of confidence intervals (ICI) rule, total-variation (TV) based method, the method based on the neighboring thresholding in the short-time Fourier transform (STFT) domain, and three wavelet-based denoising techniques by increasing the improvement in the SNR by up to 118.94% and the peak SNR by up to 138.52%, as well as by reducing the root mean squared error by up to 64.59%, the mean absolute error by up to 55.60%, and the maximum absolute error by up to 84.79%.
引力波数据（2015年首次由高级激光干涉引力波天文台（Advanced LIGO）干涉仪发现，并于2017年获得诺贝尔奖）具有非高斯和非平稳噪声的特征。获取的数据量不断增加，这就需要开发高效的去噪算法，以便能够检测出低信噪比（SNR）环境中嵌入的引力波事件。本文提出了一种基于局部多项式逼近（LPA）并结合用于滤波器支持选择的置信区间相对交集（RICI）规则的算法，用于对核心坍缩超新星产生的引力波爆发信号进行去噪。LPA-RICI去噪方法的性能在三种不同的爆发信号上进行了测试，这些信号是数值生成并注入到高级LIGO探测器收集的实际噪声数据中的。通过几个案例研究（在对应于不同SNR值的不同信号源距离下进行）获得的实验结果分析表明，LPA-RICI方法能够有效去除噪声，同时保留引力波爆发信号的形态。即使在非常低的SNR值下，该技术也能提供可靠的去噪性能。此外，分析表明，LPA-RICI方法优于将LPA与原始置信区间交集（ICI）规则相结合的方法、基于总变差（TV）的方法、基于短时傅里叶变换（STFT）域中的邻域阈值处理的方法以及三种基于小波的去噪技术，其将SNR提高了高达118.94%，峰值SNR提高了高达138.52%，同时将均方根误差降低了高达64.59%，平均绝对误差降低了高达55.60%，最大绝对误差降低了高达84.79%。
