Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion

Abstract

Efficiently performing high-resolution direction of arrival (DOA) estimation under low signal-to-noise ratio (SNR) conditions has always been a challenge task in the literatures. Obviously, in order to address this problem, the key is how to mine or reveal as much DOA related information as possible from the degraded array outputs. However, it is certain that there is no perfect solution for low SNR DOA estimation designed in the way of winner-takes-all. Therefore, this paper proposes to explore in depth the complementary DOA related information that exists in spatial spectrums acquired by different basic DOA estimators. Specifically, these basic spatial spectrums are employed as the input of multi-source information fusion model. And the multi-source information fusion model is composed of three heterogeneous meta learning machines, namely neural networks (NN), support vector machine (SVM), and random forests (RF). The final meta-spectrum can be obtained by performing a final decision-making method. Experimental results illustrate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estimators. Even under low SNR conditions, promising DOA estimation performance can be achieved.

Keywords

direction of arrival (DOA) / signal-to-noise ratio (SNR) / information fusion / meta-learning / spatial spectrum