Projection Based Beamformer Algorithm for Adaptive Beamforming in Uniform Linear Array

Adaptive beamforming is a spatial filtering technique for uniform linear array of sensors that has application in numerous fields of signal processing such as wireless communications, radar, sonar, seismology and radio astronomy. Classically the minimum-variance-distortionless-response (MVDR) beamformer provides an acceptable solution to the problem of recovering the signal-of-interest (SOI) in the array input while minimizing the array output power. A number of problems exist in practice with the MVDR beamformer due to a number of non-ideal conditions such as mismatch in the direction of arrival (DOA) of the SOI, array calibration errors, local scattering of the incident signal and the finite sample approximation of the array covariance matrix. Several adaptive beamforming techniques, which have robustness against the problems cited above, have been developed to overcome these difficulties. However, these techniques have in general high computational complexity, as they depend on the eigenvalue decomposition (EVD) of the array covariance matrix. In this work we consider the application of the multiple signal classification (MUSIC) method to the solution of the beamforming problem. This involves the estimation of the unknown DOA of the SOI based on the MUSIC algorithm. The DOA of the SOI is estimated by minimizing a cost function in terms of the norm of an error vector, which is the difference between the presumed steering vector of the SOI, and the orthogonal projection of this vector on the signal subspace. Direct implementation of this approach, however, also comprises eigenvalue decomposition of the covariance matrix. We will investigate the possibility of performing the above-mentioned minimization without EVD by expressing the cost function in terms of a parameterized estimate of the signal steering vector. Keywords: Adaptive Beamforming, Minimum-Variance-Distortionless-Response, Mismatch, Direction Of Arrival, Signal Of Interest, Eigenvalue Decomposition