An algorithm for prewhitening a large parallel line array

A steepest descent gradient algorithm prewhitens the signal received by a uniform planar array. Previously developed methods work only on single line arrays. A novel model facilitates algorithm development by reducing problems dimensionality associated with exact multi-dimensional autoregressive (AR) modeling. The discrete source model, based on a Kronecker product of the received signals between the vertical and horizontal elements of the array, agrees exactly with the classical sinusoidal model. The colored noise source Kronecker product model agrees approximately with a physical geometric one constructed from spherical surface harmonics. The algorithm uses a stacked vector parameterization of the vertical and horizontal AR parameters and optimizes them over a low order whiteness functional. Application of the algorithm with MUSIC demonstrates enhanced performance in terms of angular resolution and detection of low SNR sources. The algorithm allows extensibility and solves the general problem of the three-dimensional volumetric array with arbitrary geometry.