TY - GEN
T1 - Risk-unbiased bound for random signal estimation in the presence of unknown deterministic channel
AU - Bar, Shahar
AU - Tabrikian, Joseph
N1 - Publisher Copyright: © 2015 IEEE.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Estimation of a signal transmitted through a communication channel usually involves channel identification. This scenario can be modeled as random parameter estimation in the presence of unknown deterministic parameter. In this paper, we address the question of how accurately one can estimate a random signal intercepted by an array of sensors, subject to an unknown deterministic array response. The commonly used hybrid Cramér-Rao bound (HCRB) is restricted to mean-unbiased estimation of all model parameters with no distinction of their character and leads to optimistic and unachievable performance analysis. Instead, A Bayesian Cramér-Rao (CR)- type bound on the mean-square-error (MSE) is derived for the considered scenario. The bound is based on the risk-unbiased bound (RUB) which assumes risk-unbiased estimation of the signals of interest. Simulations show that the RUB provides a tight and achievable performance analysis for the MSE of conventional hybrid estimators.
AB - Estimation of a signal transmitted through a communication channel usually involves channel identification. This scenario can be modeled as random parameter estimation in the presence of unknown deterministic parameter. In this paper, we address the question of how accurately one can estimate a random signal intercepted by an array of sensors, subject to an unknown deterministic array response. The commonly used hybrid Cramér-Rao bound (HCRB) is restricted to mean-unbiased estimation of all model parameters with no distinction of their character and leads to optimistic and unachievable performance analysis. Instead, A Bayesian Cramér-Rao (CR)- type bound on the mean-square-error (MSE) is derived for the considered scenario. The bound is based on the risk-unbiased bound (RUB) which assumes risk-unbiased estimation of the signals of interest. Simulations show that the RUB provides a tight and achievable performance analysis for the MSE of conventional hybrid estimators.
KW - MSE
KW - combined minimum MSE-maximum likelihood (MS-ML)
KW - joint maximum a-posteriori probability-maximum likelihood (JMAP-ML)
KW - risk-unbiased bound
UR - http://www.scopus.com/inward/record.url?scp=84963818595&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2015.7383838
DO - 10.1109/CAMSAP.2015.7383838
M3 - Conference contribution
T3 - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
SP - 469
EP - 472
BT - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
T2 - 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Y2 - 13 December 2015 through 16 December 2015
ER -