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. θ = 92 • , φ = 30 • θ = 92 • , φ = 60 • θ = 92 • , φ = 90 • θ = 92 • , φ = 120 • θ = 92 • , φ = 210 • K=3 {1,
The work in REF focuses on antenna selection (AS) problem in a multi-antenna (20 antennas) system and constructs a CNN as a multi-class classification framework where each class designates a different subarray.
Cognitive Radar Antenna Selection via Deep Learning
arXiv: Signal Processing
Mathematics Computer Science Engineering