Cognitive radar antenna selection via deep learning
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Dosyalar
Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inst Engineering Technology-Iet
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Elbir, Ahmet M./0000-0003-4060-3781
WOS: 000498816400002
WOS: 000498816400002
Anahtar Kelimeler
Kaynak
Iet Radar Sonar And Navigation
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
13
Sayı
6
