Fast iterative algorithm for real-time array processing

An online iterative conjugate gradient algorithm for array processing is presented. The basic idea is to recast the array processing problem into a form so that the iterative method can be applied to compute the array weights vector recursively. To speed up the rate of convergence of the iterative process, the conjugate gradient method is used. Under moderate signal-to-noise ratios the algorithm converges to the minimum Euclidean norm least-squares solution in p iterations for p number of signals. It does not require eigendecomposition of the covariance matrix and prior information regarding the number of signals. It is capable of handling fully coherent sources and is effective for a small number of snapshots. Numerical examples are presented to illustrate the performance achievable