TY - GEN
T1 - Hardware Demonstration of a Scalable Cognitive Sparse Array
AU - Muleti, Satish
AU - Shavit, Yariv
AU - Namer, Moshe
AU - Eldar, Yonina C.
N1 - Publisher Copyright: © 2020 IEEE.
PY - 2020/9/21
Y1 - 2020/9/21
N2 - High-resolution direction of arrival estimation requires a large number of antenna elements which increases the computational cost, hardware complexity, and power requirements. To balance between hardware complexity and resolution, recently, we proposed a cognitive, scalable, sparse array selection technique based on a submodular-greedy algorithm. In this demo, we present a design and implementation of a hardware prototype that demonstrate the proposed sparse antenna selection strategy. Through real-time experiments, we show that the proposed sparse selection method results in a 2 - 3 dB lower error compared to a typically employed random selection method.
AB - High-resolution direction of arrival estimation requires a large number of antenna elements which increases the computational cost, hardware complexity, and power requirements. To balance between hardware complexity and resolution, recently, we proposed a cognitive, scalable, sparse array selection technique based on a submodular-greedy algorithm. In this demo, we present a design and implementation of a hardware prototype that demonstrate the proposed sparse antenna selection strategy. Through real-time experiments, we show that the proposed sparse selection method results in a 2 - 3 dB lower error compared to a typically employed random selection method.
UR - http://www.scopus.com/inward/record.url?scp=85098566280&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/RadarConf2043947.2020.9266620
DO - https://doi.org/10.1109/RadarConf2043947.2020.9266620
M3 - منشور من مؤتمر
SN - 978-1-7281-8943-7
T3 - IEEE National Radar Conference - Proceedings
BT - 2020 IEEE Radar Conference, RadarConf 2020
T2 - 2020 IEEE Radar Conference, RadarConf 2020
Y2 - 21 September 2020 through 25 September 2020
ER -