Feng Sheng, Hua Xiaoqiang, Zhu Xiaoqian
College of Computer Science, National University of Defense Technology, Changsha 410073, China.
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China.
Entropy (Basel). 2020 Aug 20;22(9):914. doi: 10.3390/e22090914.
In this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well judge whether the signal exists. Previous similar studies extracted insufficient information from these spectrograms, resulting in unsatisfactory detection performance especially for complex signal detection task at low signal-noise-ratio (SNR). To this end, we use a global descriptor to extract abundant features, then exploit the advantages of matrix information geometry technique by constructing the high-dimensional features as symmetric positive definite (SPD) matrices. In this case, our task for signal detection becomes a binary classification problem lying on an SPD manifold. Promoting the discrimination of heterogeneous samples through information geometric DR technique that is dedicated to SPD manifold, our proposed detector achieves satisfactory signal detection performance in low SNR cases using the K distribution simulation and the real-life sea clutter data, which can be widely used in the field of signal detection.
本文提出了一种基于矩阵信息几何降维（DR）的新型信号检测器，其灵感来源于频谱图处理。通过短时傅里叶变换（STFT），将接收到的数据表示为二维高精度频谱图，从中我们可以很好地判断信号是否存在。以往类似研究从这些频谱图中提取的信息不足，导致检测性能不尽人意，尤其是在低信噪比（SNR）下的复杂信号检测任务中。为此，我们使用全局描述符提取丰富特征，然后通过将高维特征构造为对称正定（SPD）矩阵来利用矩阵信息几何技术的优势。在这种情况下，我们的信号检测任务就变成了一个位于SPD流形上的二分类问题。通过致力于SPD流形的信息几何降维技术提高异类样本的区分度，我们提出的检测器在使用K分布模拟和实际海杂波数据的低信噪比情况下实现了令人满意的信号检测性能，可广泛应用于信号检测领域。
