Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia.
Faculty of Maritime Studies, University of Rijeka, 51000 Rijeka, Croatia.
Sensors (Basel). 2023 Apr 20;23(8):4148. doi: 10.3390/s23084148.
Compressive sensing (CS) of the signal ambiguity function (AF) and enforcing the sparsity constraint on the resulting signal time-frequency distribution (TFD) has been shown to be an efficient method for time-frequency signal processing. This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the density-based spatial clustering algorithm. Moreover, an appropriate criterion for the performance of the method is formalized, i.e., component concentration and preservation, as well as interference suppression, are measured utilizing the information obtained from the short-term and the narrow-band Rényi entropies, while component connectivity is evaluated using the number of regions with continuously-connected samples. The CS-AF area selection and reconstruction algorithm parameters are optimized using an automatic multi-objective meta-heuristic optimization method, minimizing the here-proposed combination of measures as objective functions. Consistent improvement in CS-AF area selection and TFD reconstruction performance has been achieved without requiring a priori knowledge of the input signal for multiple reconstruction algorithms. This was demonstrated for both noisy synthetic and real-life signals.
信号模糊函数（AF）的压缩感知（CS）和对所得信号时频分布（TFD）施加稀疏约束已被证明是一种有效的时频信号处理方法。本文提出了一种自适应 CS-AF 区域选择方法，该方法通过使用基于密度的空间聚类算法的聚类方法提取幅度显著的 AF 样本。此外，还正式定义了该方法的性能标准，即利用从短期和窄带 Renyi 熵中获得的信息来衡量分量的浓度和保持以及干扰抑制，而利用具有连续连接样本的区域数量来评估分量的连接性。使用自动多目标启发式优化方法优化 CS-AF 区域选择和重建算法参数，将这里提出的组合度量作为目标函数最小化。对于多种重建算法，无需先验知识即可实现 CS-AF 区域选择和 TFD 重建性能的一致改进。这在噪声合成和实际信号中都得到了证明。
