A Compact Machine Learning Architecture for Wideband Amplitude-Only Direction Finding

A generalized, reduced-size machine learning architecture for single-snapshot amplitude-only direction finding (AODF) is proposed for uniform circular arrays. A method for reusing angle of arrival (AoA) estimation models that are accurate over narrower fields of view is described. The efficacy of the proposed method is demonstrated using an ultrawideband circular array of miniaturized transverse electromagnetic (TEM) horns covering 1.5-5.5 GHz. Reasonable azimuth estimations performed on this retrofitted system are obtained over 2.6:1 bandwidth (1.5-4.0 GHz). Antenna performance features that impact the accuracy of AODF are also recognized. Root mean square error less than 5 degrees is achieved in simulation above 15 dB signal-to-noise ratio (SNR) and above 20 dB SNR in measurement. The improved accuracy over the conventional correlation method of 52%-85% is demonstrated in an SNR domain of 10-40 dB. This performance improvement is obtained while maintaining a footprint reduction of 80%-95%, and an AoA estimation time speed-up of at least 85%.

Automated summary

A generalized and reduced-size machine learning architecture is proposed for single-snapshot amplitude-only direction finding using uniform circular arrays. The method utilizes accurate angle of arrival estimation models for narrower fields of view. The efficacy of the proposed method is demonstrated using an ultrawideband circular array, achieving reasonable azimuth estimations over a wide bandwidth.