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Machine learning is a promising technique for angle-of-arrival (AOA)estimation of waves impinging a sensor array. However, the majority of themethods proposed so far only consider a known, fixed number of impinging waves,i.e., a fixed source number. This paper proposes a machine-learning-basedestimator designed for the case when the source number is variable and henceunknown a priori. The proposed estimator comprises a framework of single-labelclassifiers. Each classifier predicts if waves are present within certainrandomly selected segments of the array's field of view (FOV), resulting fromdiscretising the FOV with a certain (FOV) resolution. The classifiers'predictions are combined into a probabilistic angle spectrum, whereupon thesource number and the AOAs are estimated jointly by applying a probabilitythreshold whose optimal level is learned from data. The estimator's performanceis assessed using a new performance metric: the joint AOA estimation successrate. Numerical simulations show that for low SNR (-10 dB), a low FOVresolution (2$^\circ$) yields a higher success rate than a high resolution(1$^\circ$), whereas the opposite applies for mid (0 dB) and high (10 dB) SNRs.In nearly all simulations, except one at low SNR and a high FOV resolution, theproposed estimator outperforms the MUSIC algorithm if the maximum allowed AOAestimation error is approximately equal to (or larger than) the FOV resolution.

A Supervised Learning Framework for Joint Angle-of-Arrival and Source Number Estimation