Source number estimation based on a novel multi-view meta-hierarchical classification framework
Wu Yun, Xiukun Li, Zhimin Cao · Measurement Science and Technology · 2020
Abstract Accurately measuring the direction of arrival (DOA) is one of the most important issues in multiple sensor/antenna array monitoring scenarios. However, as a necessary parameter of almost all state-of-the-art DOA estimation methods, the source number is normally hard to determine using the traditional Akaike information criterion or minimum description length methods, especially in low or very low signal-to-noise ratio (SNR) conditions. In this paper, we propose to estimate the source number in a data-driven manner by employing a novel multi-view meta-hierarchical classification framework. Specifically, there are two collaborative views and two hierarchical classification layers employed for generating meta-features. Then, the obtained meta-features are re-learned by the final meta-classification layer to estimate the final prediction of the source number. Experimental results illustrated that the proposed method can estimate the source number accurately and reliably even in low SNR conditions.