Effective source number enumeration approach under small snapshot numbers - Ge, Shengguo (2024)
Direction of Arrival (DOA) estimation of signal sources is one of the research hotspots in the field of array signal processing. However, traditional DOA estimation methods usually require many snapshots, a high Signal-to-Noise Ratio (SNR), and a Gaussian white noise background, which are often difficult to meet in actual environments. To solve this problem, this study proposes a signal source number estimation method based on supplementary empirical mode decomposition (SEMD). The method first uses the SEMD method to decompose the array signal, decomposing the complex signal into several Intrinsic Mode Functions (IMFs), and then extracts features through these IMFs to estimate the number of signal sources. To verify the performance of the proposed SEMD method, this study designs a series of experiments, using theoretical data and measured data from a radio frequency anechoic chamber laboratory as research objects. The experimental conditions cover different snapshot numbers, SNRs, and noise backgrounds, aiming to simulate various complex environments in actual applications. Experimental results show that the SEMD-based method performs significantly better than the traditional signal source number estimation algorithm in these complex environments, especially under a small number of snapshots, the SEMD method can still maintain a high estimation accuracy. This study also makes a significant contribution to data science by providing a comprehensive method for estimating the number of signal sources, which is integrated with a machine learning model. This method overcomes the limitations of traditional methods in complex environments by combining signal processing problems with pattern recognition problems, significantly improves the accuracy of data analysis in complex environments, and provides an innovative solution for signal processing and pattern recognition in data science.