Motion compensation for adaptive horizontal line array processing

Large aperture horizontal line arrays have small resolution cells and can be used to separate a target signal from an interference signal by array beamforming. High-resolution adaptive array processing can be used to place a null at the interference signal so that the array gain can be much higher than that of conventional beamforming. But these nice features are significantly degraded by the source motion, which reduces the time period under which the environment can be considered stationary from the array processing point of view. For adaptive array processing, a large number of data samples are generally required to minimize the variance of the cross-spectral density, or the covariance matrix, between the array elements. For a moving source and interference, the penalty of integrating over a large number of samples is the spread of signal and interference energy to more than one or two eigenvalues. The signal and interference are no longer clearly identified by the eigenvectors and, consequently, the ability to suppress the interference suffers. We show in this paper that the effect of source motion can be compensated for the (signal) beam covariance matrix, thus allowing integration over a large number of data samples without loss in the signal beam power. We employ an equivalent of a rotating coordinate frame to track the signal bearing change and use the waveguide invariant theory to compensate the signal range change by frequency shifting.