Cognitive Radar Antenna Selection via Deep Learning
This addresses the challenge of reducing cost and computational load in phased array radar systems by enabling adaptive antenna selection, though it is incremental as it applies deep learning to an existing problem.
The paper tackles the problem of selecting optimal subarrays for cognitive radar to improve direction of arrival (DoA) estimation without using full arrays, resulting in a 22% better classification performance than Support Vector Machines and 72% more accurate DoA estimates than random selections.
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.