TY - GEN
T1 - Single-Sample Direction-of-Arrival Estimation by Hankel-matrix Decompositions
AU - Orfanidis, Georgios I.
AU - Pados, Dimitris A.
AU - Sklivanitis, George
AU - Bentley, Elizabeth S.
AU - Suprenant, Joseph
AU - Medley, Michael J.
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Modern networked robotic platforms operating autonomously on the ground, in the air, or in space over highfrequency bands (e.g., mm-wave or future THz) require rapid and effective estimation of the direction of arrival (DoA) of signals of interest to maintain high data rate connectivity with each other and avoid interference from external in-band sources. High robotic platform mobility limits -or completely negates- our ability to wait and collect the necessary statistically stationary sequence of antenna-array-front measurements. As a result, conventional statistical DoA estimation optimization methods may not be applicable. In this paper, we present for the first time in the literature a single-sample DoA estimation algorithm based on Hankel-matrix-representation and singular-value decomposition (SVD) of the individual antenna-array snapshot. We compare the newly proposed estimator against the Maximum Likelihood (ML) single-sample estimator of the DoA of a signal observed in white Gaussian noise and -arguably surprisingly- demonstrate significant superiority in each metric of interest, such as meansquare estimation error, bias, and variance.
AB - Modern networked robotic platforms operating autonomously on the ground, in the air, or in space over highfrequency bands (e.g., mm-wave or future THz) require rapid and effective estimation of the direction of arrival (DoA) of signals of interest to maintain high data rate connectivity with each other and avoid interference from external in-band sources. High robotic platform mobility limits -or completely negates- our ability to wait and collect the necessary statistically stationary sequence of antenna-array-front measurements. As a result, conventional statistical DoA estimation optimization methods may not be applicable. In this paper, we present for the first time in the literature a single-sample DoA estimation algorithm based on Hankel-matrix-representation and singular-value decomposition (SVD) of the individual antenna-array snapshot. We compare the newly proposed estimator against the Maximum Likelihood (ML) single-sample estimator of the DoA of a signal observed in white Gaussian noise and -arguably surprisingly- demonstrate significant superiority in each metric of interest, such as meansquare estimation error, bias, and variance.
KW - Cramér-Rao bound (CRB)
KW - Hankel matrices
KW - direction of arrival (DoA) estimation
KW - maximum likelihood estimation
KW - sensor arrays
KW - small sample support
UR - https://www.scopus.com/pages/publications/85150176409
U2 - 10.1109/IEEECONF56349.2022.10051870
DO - 10.1109/IEEECONF56349.2022.10051870
M3 - Conference contribution
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1026
EP - 1030
BT - Conference Record of the 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Y2 - 31 October 2022 through 2 November 2022
ER -