脉冲噪声下基于改良Kendall’s Tau的随机信号检测
Random Signal Detection Based on Improved Kendall’s Tau under Impulsive Noise
DOI: 10.12677/jisp.2024.134037, PDF,    科研立项经费支持
作者: 肖芷菁, 徐维超*:广东工业大学自动化学院,广东 广州;赖华东:广东海洋大学电子与信息工程学院,广东 湛江
关键词: Kendall’s Tau高斯混合模型随机信号检测接收机工作特性曲线Kendall’s Tau Gaussian Mixture Model (GMM) Random Signal Detection Receiver Operating Characteristic (ROC) Curve
摘要: 在双通道信号检测领域,肯德尔秩相关系数(Kendall’s Tau, KT)作为一种检测器,对含脉冲噪声的信号具有显著的鲁棒性。然而,当通道间的噪声存在相关性时,KT的检测性能仍有待提升。为此,本文提出一种改进的肯德尔秩相关系数(Improved Kendall’s Tau, IKT)检测器,在KT的基础上引入了阈值可调节的硬限幅函数。同时采用二元高斯混合模型(Gaussian Mixture Model, GMM)模拟两通道间噪声的相关性及脉冲特性,深入探讨了IKT在该模型下的统计性质,并建立了针对双通道高斯随机信号检测问题的虚警率和检测概率的解析式。通过蒙特卡罗实验与高斯噪声下性能最优的匹配滤波器(Matched Filter Detector, MFD)、脉冲噪声下具有鲁棒性的极性重合相关器(Polarity Coincidence Correlator, PCC)、KT的接收机工作特性(Receiver Operating Characteristic, ROC)曲线下面积(Area Under the Curve, AUC)进行比较,表明IKT在含相关性高斯噪声的信号检测中相较于PCC在AUC上表现出12.9%左右的提升,相较于KT的提升约为4.8%。在含相关性脉冲噪声的信号检测中相较于PCC的AUC提升约为8.3%,相较于KT的提升约为1.6%,从而验证了其优越性。
Abstract: In dual-channel signal detection, the Kendall’s Tau (KT) correlation coefficient is well-regarded for its robustness in handling signals affected by impulsive noise. However, its detection performance declines when there is noise correlation between channels. To address this limitation, this paper presents an Improved Kendall’s Tau (IKT) detector, which enhances the traditional KT by incorporating a threshold-adjustable hard limiting function. Furthermore, a bivariate Gaussian Mixture Model (GMM) is used to simulate the noise correlation and impulsive characteristics between the two channels. The statistical properties of IKT under this model are thoroughly analyzed, and analytical expressions for the false alarm rate and detection probability in dual-channel Gaussian random signal detection are derived. Monte Carlo simulations and comparisons with the matched filter detector (MFD), which is optimal for Gaussian noise, the polarity coincidence correlator (PCC), known for its robustness against impulsive noise, and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for KT, are performed. The results show that in the presence of correlated Gaussian noise, IKT achieves approximately a 12.9% improvement in AUC over PCC and a 4.8 % improvement over KT. In the presence of correlated impulsive noise, IKT shows about an 8.3% improvement in AUC over PCC and a 1.6% improvement over KT, thereby validating its superiority.
文章引用:肖芷菁, 赖华东, 徐维超. 脉冲噪声下基于改良Kendall’s Tau的随机信号检测[J]. 图像与信号处理, 2024, 13(4): 427-439. https://doi.org/10.12677/jisp.2024.134037

参考文献

[1] Yang, S., Yi, W., Jakobsson, A., Wang, Y. and Xiao, H. (2023) Weak Signal Detection with Low-Bit Quantization in Colocated MIMO Radar. IEEE Transactions on Signal Processing, 71, 447-460. [Google Scholar] [CrossRef
[2] Zhang, L., Huanq, J., Jin, Y., Hau, Y., Jianq, M. and Zhang, Q. (2010) Waveform Diversity Based Sonar System for Target Localization. Journal of Systems Engineering and Electronics, 21, 186-190. [Google Scholar] [CrossRef
[3] 王旭东, 刘渝. 多通道自相关信号检测算法及其FPGA实现[J]. 仪器仪表学报, 2007, 28(5): 875-881.
[4] Tao, Q., Zhong, C., Chen, X., Lin, H. and Zhang, Z. (2019) Maximum-eigenvalue Detector for Multiple Antenna Ambient Backscatter Communication Systems. IEEE Transactions on Vehicular Technology, 68, 12411-12415. [Google Scholar] [CrossRef
[5] Liu, C., Wang, J., Liu, X. and Liang, Y. (2019) Deep CM-CNN for Spectrum Sensing in Cognitive Radio. IEEE Journal on Selected Areas in Communications, 37, 2306-2321. [Google Scholar] [CrossRef
[6] 李贺, 赵文静, 罗雪松, 刘畅, 邹德岳, 金明录. 基于特征值拟合优度的频谱感知算法研究[J]. 大连理工大学学报, 2020, 60(6): 635-641.
[7] 郑作虎, 王首勇. 基于Alpha稳定分布杂波模型的雷达目标检测方法[J]. 电子与信息学报, 2014, 36(12): 2963-2968.
[8] Kostylev, V. and Gres, I. (2018) Characteristics of p-Norm Signal Detection in Gaussian Mixture Noise. IEEE Transactions on Vehicular Technology, 67, 2973-2981. [Google Scholar] [CrossRef
[9] Sun, L., Cao, Y., Wu, W. and Liu, Y. (2020) A Multi-Target Tracking Algorithm Based on Gaussian Mixture Model. Journal of Systems Engineering and Electronics, 31, 482-487. [Google Scholar] [CrossRef
[10] Zhu, X., Wang, T., Bao, Y., Hu, F. and Li, S. (2019) Signal Detection in Generalized Gaussian Distribution Noise with Nakagami Fading Channel. IEEE Access, 7, 23120-23126. [Google Scholar] [CrossRef
[11] 朱晓梅, 蒋培, 包亚萍. 高斯混合噪声环境中基于分数低阶矩的频谱感知算法研究[J]. 信号处理, 2015, 31(8): 968-974.
[12] Wang, P., Zhang, R., Yan, Y., Hao, D., Huang, N., Wu, Q. and An, Z. (2013) An Acquisition System of Digital Nuclear Signal Processing for Algorithm Development. Nuclear Science and Techniques, 24, 115-121.
[13] Ma, R., Xu, W., Wang, Q. and Chen, W. (2014) Robustness Analysis of Three Classical Correlation Coefficients under Contaminated Gaussian Model. Signal Processing, 104, 51-58. [Google Scholar] [CrossRef
[14] Xu, W., Hou, Y., Hung, Y.S. and Zou, Y. (2013) A Comparative Analysis of Spearman’s Rho and Kendall’s Tau in Normal and Contaminated Normal Models. Signal Processing, 93, 261-276. [Google Scholar] [CrossRef
[15] 王彦光, 朱鸿斌, 徐维超. ROC曲线及其分析方法综述[J]. 广东工业大学学报, 2021, 38(1): 46-53.
[16] Lai, H. and Xu, W. (2019) Statistical Properties of Average Kendall’s Tau under Multivariate Contaminated Gaussian Model. IEEE Access, 7, 159177-159189. [Google Scholar] [CrossRef
[17] Xu, W., Chen, C., Dai, J., Zhou, Y. and Zhang, Y. (2019) Detection of Known Signals in Additive Impulsive Noise Based on Spearman’s Rho and Kendall’s Tau. Signal Processing, 161, 165-179. [Google Scholar] [CrossRef
[18] 黄海深, 徐维超. 基尼相关在有色噪声中的性能分析[J]. 电子世界, 2021(7): 47-48.