A deep-learning-based approach to estimating the number of coherent sources in radar is presented. A proper estimate of the number of sources in a signal enables improved angle-of-arrival (AoA) estimation common in applications such as radar, sonar, and communication systems. Many AoA estimators utilised in these areas require accurate estimates of the number of sources for enhanced performance. Herein, a robust method that performs well under the existence of coherent sources is developed. The

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... proposed method is based on deep learning and it is shown to perform better than state-of-the-art versions of the Akaike Information Criteria (AIC), Minimum Description Length (MDL), and Exponentially Embedded Families (EEF) estimators, which employ spatial smoothing of the covariance matrix to handle coherent signals. In radar signal processing, estimating the number of signals present in noisy data is a difficult problem which has been explored extensively. However, robust solutions in this area are This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 15:431-440. wileyonlinelibrary.com/journal/rsn2 -431 Note: Best results in bold. Abbreviations: C,SC = cov., smoothed cov.; E,SE = eigenvalues cov., and smoothed cov.; C&E,SC&SE = cov. and eigs., smoothed cov. and eigs.; OA = Overall Accuracy; UE = Underestimated; OE = Overestimated. - ROGERS ET AL.