A Computational System-Level Evaluation of a Multi-Octave Omnidirectional Direction-Finding System Using Full-Wave Electromagnetic Modeling of a Biconical Array and High-Fidelity Digital Receiver Simulation

A deep neural network-based 2D multi-octave omnidirectional direction-finding (DNN-based MOOD-DF) framework was previously developed using seven isotropic antenna elements arranged in a randomly distributed 3D array together with a simplified digital receiver model. In this paper, a system-level evaluation of the corresponding DF system is performed using a full-wave electromagnetic 3D DF array and high-fidelity receiver model. The isotropic elements and simplified receiver models are replaced with 2–18 GHz biconical antenna elements and high-fidelity digital receiver models that account for array feed structure effects, mutual coupling, receiver noise, cable length mismatches, and antenna receiver impedance mismatches within the RF receiving chain. Because full-array multi-octave electromagnetic simulations are computationally intensive, three representative frequency blocks (2–4 GHz, 10–12 GHz, and 16–18 GHz), spanning the lower, middle, and upper portions of the 2–18 GHz frequency range, are selected for DF system performance evaluation. The results demonstrate that the proposed DF system remains effective when trained and tested using the high-fidelity antenna array and receiver models. Across the investigated frequency bands, the system achieves sub-degree azimuth estimation accuracy and elevation RMSEs below approximately 1.8° at SNRs near 0 dB while maintaining microsecond-scale inference latency suitable for real-time applications. Although a performance gap relative to the Cramér–Rao lower bound remains, benchmark comparisons with the spectral MUSIC algorithm at selected frequencies indicate that the proposed approach can successfully operate on the complex and highly spatially aliased 3D manifold of the multi-octave biconical array, whereas conventional narrowband DF techniques may encounter substantial challenges under the same conditions. The presented end-to-end modeling and validation framework of the DF system provides a bridge between previous idealized studies and future hardware prototyping and experimental validation.