A SLIDE-SAVE BASED FRAMEWORK FOR MULTI-SOURCE DOA EXTRACTION WITH SPATIAL CLOSELY SEPARATED SOURCES

In adjacent sources scenarios, the low angular separation between active sources may degrade the performance of direction-of-arrival (DOA) estimation. In this work, we propose a slide-save based framework to address the problem of extracting multi-source DOAs for spatial closely separated sources. The basic idea is to identify the DOA estimates corresponding to the locally most dominant source within a sliding time-frequency (TF) window. Three different schemes are introduced to determine the critical DOA estimates in each TF window. The final DOAs are extracted using the retained DOA estimates by extending the histogram-based, clustering-based and Gaussian Mixture Model (GMM)-based multi-source DOA extraction methods. In addition, other intensity-based algorithms can also be incorporated into the proposed framework. Simulation results show that the proposed framework is effective to estimate multi-source DOAs in adjacent sources scenarios.