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A feedforward neural network for direction-of-arrival estimation.

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A new nonlinear deep feed-forward neural network (FNN) accurately estimates direction-of-arrival (DOA) for multiple sources, even with an unknown number of signals. This method offers performance comparable to existing techniques using fewer snapshots.

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Acoustics

Background:

  • Conventional beamforming is a standard technique for direction-of-arrival (DOA) estimation.
  • Linear supervised learning models offer alternative approaches to DOA estimation.
  • Deep learning presents opportunities for advancing DOA estimation capabilities.

Purpose of the Study:

  • To reformulate conventional beamforming as a linear inverse problem.
  • To develop a nonlinear deep feed-forward neural network (FNN) for DOA estimation.
  • To evaluate the performance of the FNN for single and multiple source scenarios, including unknown source counts.

Main Methods:

  • Conventional beamforming reformulated as a real-valued, linear inverse problem.
  • Development of a nonlinear deep feed-forward neural network (FNN).
  • Hyperparameter search for FNN optimization and two training methodologies (exhaustive and random).

Main Results:

  • Linear formulation provided quick and accurate DOA estimation.
  • The nonlinear FNN resolved incoherent sources with 1° resolution using a single snapshot.
  • K-source FNN achieved performance comparable to Multiple Signal Classification and Sparse Bayesian Learning with multiple snapshots for an unknown number of sources.
  • Demonstrated practicality on Swellex96 experimental data.

Conclusions:

  • The developed nonlinear deep FNN is a viable and effective method for direction-of-arrival estimation.
  • The FNN model shows promise for handling complex scenarios with multiple, potentially unknown, sources.
  • This approach offers competitive performance against established methods in DOA estimation.