Multidimensional rank reduction estimator for parametric MIMO channel models
EURASIP Journal on Applied Signal Processing
Multisource self-calibration for sensor arrays
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Robust adaptive beamforming in partly calibrated sparse sensor arrays
IEEE Transactions on Signal Processing
Improved MUSIC by exploiting both real and complex sources
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
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We consider the direction-finding problem in partly calibrated arrays composed of several calibrated and identically oriented (but possibly nonidentical) subarrays that are displaced by unknown (and possibly time-varying) vector translations. A new search-free eigenstructure-based direction-finding approach is proposed for such class of sensor arrays. It is referred to as the rank-reduction (RARE) estimator and enjoys simple implementation that entails computing the eigendecomposition of the sample array covariance matrix and polynomial rooting. Closed-form expressions for the deterministic Cramer-Rao bounds (CRBs) on direction-of-arrival (DOA) estimation for the considered class of sensor arrays are derived. Comparison of these expressions with simulation results show that the finite-sample performance of RARE algorithms in both time-invariant and time-varying array cases is close to the corresponding bounds. Moreover, comparisons of the derived CRBs with the well-known bounds for the fully calibrated time-invariant array case help to discover several interesting properties of DOA estimation in partly calibrated and time-varying arrays.