文献类型：期刊文献

英文题名：Pearson Correlation Fusion Sensing Method for Noncircular Signals

作者：Huadong, Lai[1];Cong, Lin[1];Peng, Luo[1];Jinqiang, Xu[1];Mingxin, Liu[1];Weichao, Xu[2]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Univ Technol, Sch Automat, Dept Automatic Control, Guangzhou 510006, Peoples R China

年份：2026

卷号：48

期号：6

起止页码：2419

外文期刊名：JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY

收录：ESCI(收录号：WOS:001880447400010)、WOS

基金：Foundation Items: The National Natural Science Foundation of China (62501175, 62171143) , The Natural Science Foundation of Guangdong Province (2026A1515011437)

语种：英文

外文关键词：Spectrum sensing; Noncircular signals; Pearson Correlation Coefficient(PCC); Data fusion

外文摘要：Objective With the rapid growth of wireless devices and communication services, spectrum resources have become increasingly scarce. Spectrum sensing, as a fundamental function of cognitive radio, enables dynamic spectrum access and improves spectrum utilization efficiency. However, conventional spectrum sensing methods based on circular signal assumptions cannot effectively detect noncircular signals. In addition, some detectors designed for noncircular signals show degraded performance under low Signal-to-Noise Ratio (SNR) or limited sample conditions. To address these limitations, a nonparametric spectrum sensing scheme based on the Weighted Pearson Correlation Coefficient (WPCC) is proposed. The scheme applies a linear fusion strategy to the real-valued composite coherence matrix, which captures the second-order statistical characteristics of noncircular signals. Methods The WPCC detector constructs a real-valued composite observation vector and computes the corresponding composite coherence matrix. Pearson Correlation Coefficients (PCCs) are extracted from this matrix to characterize the statistical properties of noncircular signals. The first two product moments of squared sample PCCs are derived, and optimal fusion weights are obtained based on the deflection coefficient. The true PCCs are approximated by their sample estimates to obtain data-driven fusion weights that do not require prior knowledge of sensing channels. These weights are then linearly combined with the squared sample PCCs to construct the WPCC test statistic, thereby exploiting the spatial diversity of sensing antennas. The final decision is made by comparing the WPCC statistic with a sensing threshold determined by the specified false alarm probability. Specifically, a WPCC value below the threshold indicates the null hypothesis of an idle frequency band, whereas a value above the threshold indicates the alternative hypothesis that the frequency band is occupied by primary users. Results and Discussions Simulation experiments evaluate the sensing performance of the proposed nonparametric WPCC-based method (Algorithm 1) in terms of sensing probability, deflection coefficient, Receiver Operating Characteristic (ROC) curve, and Area Under the Curve (AUC), with comparisons to NCLMPIT, NCAGM, NCHDM, and NCJT. The numerical results show that the proposed method outperforms the compared detectors under various simulation conditions. In particular, the WPCC detector achieves the highest sensing probability and exhibits superior performance at low false alarm probabilities of 0.05 (Fig. 2), 0.01 (Fig. 3(a)), and 0.005 (Fig. 3(b)), with sample sizes not exceeding 100. In addition, the proposed method shows clear advantages under different numbers of antennas (Fig. 4), different noise variance conditions (Fig. 5), and different levels of correlation strength (Fig. 6). The applicability of the WPCC method to circular signals is also demonstrated by its high sensing probability for QPSK and 16PSK signals (Fig. 7). The superior overall performance of the proposed detector is further confirmed by higher deflection coefficient curves and ROC curves (Figs. 8, 9). The largest AUC values quantitatively demonstrate its overall optimality among all considered methods (Table 1). These results indicate strong robustness under low SNR and small-sample conditions. Conclusions A Pearson correlation fusion sensing method for noncircular signals is proposed based on the real-valued composite covariance representation and the Locally Most Powerful Invariant Test (LMPIT) framework. By combining optimal fusion weights derived from sample PCCs with a linear weighting scheme, the method fully exploits second-order statistical information. It enhances strongly correlated components while suppressing weak correlations and noise interference. Analytical expressions for the false alarm probability and sensing threshold are derived. Both theoretical analysis and simulation results show that the proposed method achieves superior performance compared with existing noncircular signal sensing methods in terms of sensing probability, deflection coefficient, ROC curve, and AUC.