TY - GEN
T1 - Ziv-Zakai Bound for Wideband Single-Source Direction-of-Arrival Estimation
AU - Wang, Yizhe
AU - Peng, Yuning
AU - Liao, Chenxi
AU - Wang, Min
AU - Shen, Qing
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The Ziv-Zakai bound (ZZB) for wideband Direction- of-Arrival (DOA) estimation of a single source within the subband model is examined. The study involves deriving the regularity condition and determining the minimum error probability, ultimately leading to a closed-form expression of the ZZB. This expression is dependent on several factors: the prior DOA distribution, the number of snapshots, signal wavelength, the quantity of involved subbands, and the signal-to-noise ratio (SNR). In scenarios with high SNR or a large number of snapshots, the estimation error decreases, causing the ZZB to approach the well- known Cramér-Rao bound (CRB). Conversely, in the a priori performance region, where the number of snapshots or SNR is low, the estimation error tends to be distributed across the entire prior parameter space, with its boundary approaching the prior covariance. The ZZB theoretically analyzes and derives a tighter bound, providing a more effective reference for the analysis of estimation errors.Simulation results indicate that the ZZB effectively predicts performance across all regions of interest.
AB - The Ziv-Zakai bound (ZZB) for wideband Direction- of-Arrival (DOA) estimation of a single source within the subband model is examined. The study involves deriving the regularity condition and determining the minimum error probability, ultimately leading to a closed-form expression of the ZZB. This expression is dependent on several factors: the prior DOA distribution, the number of snapshots, signal wavelength, the quantity of involved subbands, and the signal-to-noise ratio (SNR). In scenarios with high SNR or a large number of snapshots, the estimation error decreases, causing the ZZB to approach the well- known Cramér-Rao bound (CRB). Conversely, in the a priori performance region, where the number of snapshots or SNR is low, the estimation error tends to be distributed across the entire prior parameter space, with its boundary approaching the prior covariance. The ZZB theoretically analyzes and derives a tighter bound, providing a more effective reference for the analysis of estimation errors.Simulation results indicate that the ZZB effectively predicts performance across all regions of interest.
KW - DOA estimation
KW - subband decomposition model
KW - wideband signal
KW - Ziv-Zakai bound
UR - http://www.scopus.com/inward/record.url?scp=86000028559&partnerID=8YFLogxK
U2 - 10.1109/ICSIDP62679.2024.10868000
DO - 10.1109/ICSIDP62679.2024.10868000
M3 - Conference contribution
AN - SCOPUS:86000028559
T3 - IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
BT - IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Y2 - 22 November 2024 through 24 November 2024
ER -