2023/04/18 by Stefan Feintuch, Joseph Tabrikian, Igal Bilik +2 · 2 citations
Computer Science · Earth and Planetary Sciences · #Blind Source Separation Techniques #Underwater Acoustics Research #Target Tracking and Data Fusion in Sensor Networks
paper · doi:10.1109/taes.2023.3268256
This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of non-Gaussian, heavy-tailed, and spatially-colored interference. Conventionally, the interference is considered to be Gaussian-distributed and spatially white. However, in practice, this assumption is not guaranteed, which results in degraded DOA estimation performance. Maximum likelihood DOA estimation in the presence of non-Gaussian and spatially-colored interference is computationally complex and not practical. Therefore, this work proposes a neural network (NN)-based DOA estimation approach for spatial spectrum estimation in multisource scenarios with an a priori unknown number of sources in the presence of non-Gaussian spatially-colored interference. The proposed approach utilizes a single NN instance for simultaneous source enumeration and DOA estimation. It is shown via simulations that the proposed approach significantly outperforms conventional and NN-based approaches in terms of probability of resolution, estimation accuracy, and source enumeration accuracy in conditions of low signal-to-interference ratio, small-sample support, and when the angular separation between the source DOAs and the spatially-colored interference is small.