Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interference

Research output: Contribution to journalArticlepeer-review


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 multi-source scenarios with <italic>a-priori</italic> 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 SIR, small sample support, and when the angular separation between the source DOAs and the spatially-colored interference is small.

Original languageEnglish
Pages (from-to)1-30
Number of pages30
JournalIEEE Transactions on Aerospace and Electronic Systems
StatePublished - 18 Apr 2023


  • AIC
  • Array Processing
  • Covariance matrices
  • DOA Estimation
  • Deep Learning
  • Direction-of-arrival estimation
  • Estimation
  • Heavily-tailed distribution
  • Interference
  • MDL
  • MVDR
  • Machine Learning
  • Neural Networks
  • Non-Gaussian Interference
  • Radar
  • Source Enumeration
  • Spatial resolution
  • Spatially-Colored Interference
  • Thermal noise

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering


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