TY - GEN
T1 - Robust Semiparametric DOA Estimation in non-Gaussian Environment
AU - Fortunati, Stefano
AU - Renaux, Alexandre
AU - Pascal, Frederic
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/21
Y1 - 2020/9/21
N2 - A general non-Gaussian semiparametric model is adopted to characterize the measurement vectors, or snapshots, collected by a linear array. Moreover, the recently derived robust semiparametric efficient R-estimator of the data covariance matrix is exploited to implement an original version of the MUSIC estimator. The efficiency of the resulting R-MUSIC algorithm is investigated by comparing its Mean Squared Error (MSE) in the estimation of the source spatial frequencies with the relevant Semiparametric Stochastic Cramér-Rao Bound (SSCRB).
AB - A general non-Gaussian semiparametric model is adopted to characterize the measurement vectors, or snapshots, collected by a linear array. Moreover, the recently derived robust semiparametric efficient R-estimator of the data covariance matrix is exploited to implement an original version of the MUSIC estimator. The efficiency of the resulting R-MUSIC algorithm is investigated by comparing its Mean Squared Error (MSE) in the estimation of the source spatial frequencies with the relevant Semiparametric Stochastic Cramér-Rao Bound (SSCRB).
KW - MUSIC algorithm
KW - Semiparametric Stochastic Cramér-Rao Bound
KW - Semiparametric models
KW - robust covariance matrix estimation
UR - https://www.scopus.com/pages/publications/85098588855
U2 - 10.1109/RadarConf2043947.2020.9266451
DO - 10.1109/RadarConf2043947.2020.9266451
M3 - Conference contribution
AN - SCOPUS:85098588855
T3 - IEEE National Radar Conference - Proceedings
BT - 2020 IEEE Radar Conference, RadarConf 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Radar Conference, RadarConf 2020
Y2 - 21 September 2020 through 25 September 2020
ER -