TY - GEN
T1 - Angle-of-Arrival Estimation in Antenna Arrays based on Monopulse Signal
AU - Song, Ha Lim
AU - Nam, Sung Sik
AU - Ko, Young Chai
N1 - Funding Information:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2015-0-00385) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)
Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/7
Y1 - 2019/7
N2 - Angle-of-Arrival (AoA) estimation supports beamforming that controls array beams in a desired direction while eliminating interference signals. In this paper, we propose a blind AoA estimation method with low complexity by applying a monopulse radar processing method to an antenna array. This paper aims to investigate the statistical characteristics of monopulse signal R for the performance analysis. Monopulse signal ratio R is modeled as the ratio of quadratic random variables in the form of \mathrm{x}^{\prime}\mathrm{Ax}/\mathrm{x}^{\prime}\mathrm{Bx}, where A and B are indefinite and positive semidefinite matrices, respectively. In this paper we obtain the probability density function (PDF) of R. Furthermore, we confirm that the monopulse based AoA estimator (MAoA) is comparable to the single-target estimation performance of subspace-based algorithms with high complexity such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance technique (ESPRIT) in terms of mean square error (MSE).
AB - Angle-of-Arrival (AoA) estimation supports beamforming that controls array beams in a desired direction while eliminating interference signals. In this paper, we propose a blind AoA estimation method with low complexity by applying a monopulse radar processing method to an antenna array. This paper aims to investigate the statistical characteristics of monopulse signal R for the performance analysis. Monopulse signal ratio R is modeled as the ratio of quadratic random variables in the form of \mathrm{x}^{\prime}\mathrm{Ax}/\mathrm{x}^{\prime}\mathrm{Bx}, where A and B are indefinite and positive semidefinite matrices, respectively. In this paper we obtain the probability density function (PDF) of R. Furthermore, we confirm that the monopulse based AoA estimator (MAoA) is comparable to the single-target estimation performance of subspace-based algorithms with high complexity such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance technique (ESPRIT) in terms of mean square error (MSE).
KW - AoA estimation
KW - array processing
KW - beamforming
KW - monopulse
UR - http://www.scopus.com/inward/record.url?scp=85071857572&partnerID=8YFLogxK
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U2 - 10.1109/ICUFN.2019.8806061
DO - 10.1109/ICUFN.2019.8806061
M3 - Conference contribution
AN - SCOPUS:85071857572
T3 - International Conference on Ubiquitous and Future Networks, ICUFN
SP - 610
EP - 613
BT - ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks
PB - IEEE Computer Society
T2 - 11th International Conference on Ubiquitous and Future Networks, ICUFN 2019
Y2 - 2 July 2019 through 5 July 2019
ER -