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    CNN-based cognitive radar array selection
    (Institute of Electrical and Electronics Engineers Inc., 2019) Elbir, Ahmet Musab; Mishra, Kumar Vijay; Eldar, Yonina
    In cognitive radar, it may be desired to select an optimal subarray from a full antenna array in each scan to reduce the cost and computational complexity. Previous works on antenna selection rely on mostly optimization or greedy search methods. In this paper, we introduce a deep learning approach for antenna selection in a cognitive radar scenario. We design a deep convolutional neural network (CNN) to select the best subarray for direction-of-arrival estimation for each scan. The CNN accepts the array covariance matrix as its input and, unlike previous works, does not require prior knowledge about the target location. The performance of the proposed CNN approach is evaluated through numerical simulations. In particular, we show that it provides more accurate results than conventional support vector machines. © 2019 IEEE.
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    Cognitive radar antenna selection via deep learning
    (Inst Engineering Technology-Iet, 2019) Elbir, Ahmet Musab; Mishra, Kumar Vijay; Eldar, Yonina
    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 a 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 optimisation and greedy search methods to pick the best subarrays cognitively. In this study, deep learning is leveraged to address the antenna selection problem. Specifically, they 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. Their 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.