TY - GEN
T1 - Cramer-Rao Bound on DOA Estimation of Finite Bandwidth Signals Using a Moving Sensor
AU - Arora, Aakash
AU - Bhavani Shankar Mysore, R.
AU - Ottersten, Bjorn
N1 - Funding Information:
This work is supported by the National Research Fund (FNR), Luxembourg under the AFR-PPP grant for Ph.D. project SPASAT (Ref.: 11607283), and the CORE-PPP project PROSAT.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - In this paper, we provide a framework for the direction of arrival (DOA) estimation using a single moving sensor and evaluate performance bounds on estimation. We introduce a signal model which captures spatio-temporal incoherency in the received signal due to sensor motion in space and finite bandwidth of the signal, hitherto not considered. We show that in such a scenario, the source signal covariance matrix becomes a function of the source DOA, which is usually not the case. Due to this unknown dependency, traditional subspace techniques cannot be applied and conditions on source covariance needs to imposed to ensure identifiability. This motivates us to investigate the performance bounds through the Cramer-Rao Lower Bounds (CRLBs) to set benchmark performance for future estimators. This paper exploits the signal model to derive an appropriate CRLB, which is shown to be better than those in relevant literature.
AB - In this paper, we provide a framework for the direction of arrival (DOA) estimation using a single moving sensor and evaluate performance bounds on estimation. We introduce a signal model which captures spatio-temporal incoherency in the received signal due to sensor motion in space and finite bandwidth of the signal, hitherto not considered. We show that in such a scenario, the source signal covariance matrix becomes a function of the source DOA, which is usually not the case. Due to this unknown dependency, traditional subspace techniques cannot be applied and conditions on source covariance needs to imposed to ensure identifiability. This motivates us to investigate the performance bounds through the Cramer-Rao Lower Bounds (CRLBs) to set benchmark performance for future estimators. This paper exploits the signal model to derive an appropriate CRLB, which is shown to be better than those in relevant literature.
KW - DOA estimation
KW - incoherence
KW - moving sensor
KW - multiplicative noise
KW - sub-diagonal sums
KW - n/a OA procedure
UR - https://www.scopus.com/pages/publications/85089210523
U2 - 10.1109/ICASSP40776.2020.9054105
DO - 10.1109/ICASSP40776.2020.9054105
M3 - Conference contribution
AN - SCOPUS:85089210523
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4697
EP - 4701
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - IEEE
T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -