  In this paper, we address the problem of direction of arrival (DOA)
estimation for multiple targets in the presence of sensor failures in a sparse
array. Generally, sparse arrays are known with very high-resolution
capabilities, where N physical sensors can resolve up to $\mathcal{O}(N^2)$
uncorrelated sources. However, among the many configurations introduced in the
literature, the arrays that provide the largest hole-free co-array are the most
susceptible to sensor failures. We propose here two machine learning (ML)
methods to mitigate the effect of sensor failures and maintain the DOA
estimation performance and resolution. The first method enhances the
conventional spatial smoothing using deep neural network (DNN), while the
second one is an end-to-end data-driven method. Numerical results show that
both approaches can significantly improve the performance of MRA with two
failed sensors. The data-driven method can maintain the performance of the
array with no failures at high signal-tonoise ratio (SNR). Moreover, both
approaches can even perform better than the original array at low SNR thanks to
the denoising effect of the proposed DNN
