Versatile robust Capon beamforming: theory and applications

The standard Capon beamformer (SCB) has better resolution and much better interference rejection capability than the data-independent beamformer provided that the array steering vector corresponding to the signal-of-interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the data-independent beamformer. Most of the early suggested robust adaptive methods are rather ad hoc in that the choice of their parameters are not directly related to the uncertainly of the steering vector. In this paper we provide a review of the recently proposed robust Capon beam-former (RCB) and doubly constrained robust Capon beamformer (DCRCB), which directly address the uncertainty of the steering vector and naturally extend the covariance fitting formulation of SCB to the case of uncertain steering vectors by enforcing a double constraint on the steering vector, viz. a constant norm constraint and an uncertainty set constraint. We also present several extensions and applications of RCB including constant-powerwidth RCB (CPRCB) and constant-beamwidth RCB (CBRCB) for acoustic imaging, rank-deficient robust Capon filter-bank (RCF) approach for spectral estimation, and rank-deficient RCB for landmine detection using forward-looking ground penetrating radar (FLGPR) imaging systems. The excellent performances of RCB, DCRCB, and the various extensions of RCB are demonstrated by simulated and experimental examples.