Deep Neural Network Model of Ultrafast 2D Direction-of-Arrival Estimation Using Planar Arrays for Multi-Octave-Band Digital Receiver Applications

This study presents a deep neural network (DNN) model for multi-octave-band direction-finding (MOB-DF) estimation using a broadband DF-array and multi-layer perceptron (MLP). The model leverages randomly placed array elements to generate unique array steering vectors (ASVs) for directions within a cone-shaped field-of-view. By directly linking ASVs and signal frequency to direction via an MLP, it eliminates reliance on the signal covariance matrix, a common component in many 2D neural network-based DF methods. The DNN-based MOB-DF model is structured into sub-bands, each utilising a trained 16 × 1024 MLP. Simulations with 3-, 4-, and 5-element DF models, trained and validated on datasets with signal-to-noise ratios (SNRs) of 10, 20, and 100 dB respectively, reveal several key findings: (1) MLPs trained at 10 dB SNR can achieve better estimation performance across varying SNR levels, where estimation performance is defined as the probability of direction estimation error ≤ 1°. (2) Increasing array elements expands MOB coverage. (3) The 5-element model attains probabilities of 50% and 90% for ≤ 1° estimation errors at approximately −20 and −10 dB SNR respectively within 2–20 GHz. (4) Average prediction time per direction is on the microsecond scale. (5) The model shows resilience to frequency estimation uncertainties.