Efficient signal detection in perturbed arrays

The use of sensor arrays in signal processing applications has received considerable attention. Various array perturbations caused by phase, calibration, or modeling errors often cause the sensor observations to become only partially correlated, limiting the application of traditional matched-field beamformers. Quadratic array processing is optimal for many randomly perturbed array problems; however, direct implementation poses a significant computational burden. We propose a highly efficient, asymptotically optimal method of implementing quadratic array processors suitable for detection problems in randomly perturbed arrays. Specifically, we show that under certain conditions the optimal array processor can be approximately realized efficiently and robustly employing only discrete Fourier transforms to deal with spatial processing while entailing only a small loss in performance.