  Direction of arrival (DoA) estimation of targets improves with the number of
elements employed by a phased array radar antenna. Since larger arrays have
high associated cost, area and computational load, there is recent interest in
thinning the antenna arrays without loss of far-field DoA accuracy. In this
context, a cognitive radar may deploy a full array and then select an optimal
subarray to transmit and receive the signals in response to changes in the
target environment. Prior works have used optimization and greedy search
methods to pick the best subarrays cognitively. In this paper, we leverage deep
learning to address the antenna selection problem. Specifically, we construct a
convolutional neural network (CNN) as a multi-class classification framework
where each class designates a different subarray. The proposed network
determines a new array every time data is received by the radar, thereby making
antenna selection a cognitive operation. Our numerical experiments show that
{the proposed CNN structure provides 22% better classification performance than
a Support Vector Machine and the resulting subarrays yield 72% more accurate
DoA estimates than random array selections.
