Target detector in sea clutter background based on maximum eigenvalue of dual-channel data and filtering processing

Abstract

Effectively harnessing the correlation information within data through the covariance matrix, a geometrically informed matrix constant false alarm detector proves proficient in target detection amidst sea clutter, employing a limited number of millimeter-wave pulses. However, current matrix CFAR detectors solely rely on a single data channel, exhibiting high computational complexity, thus resulting in diminished utilization of correlation information and constrained detection scenarios. This letter proposes a low-complexity detector based on the maximum eigenvalue derived from dual-channel data. Leveraging Fast Fourier Transform, the authors preprocess data from two channels, construct eigenvalues of the cross-covariance matrix to capture correlations, and employ the maximum eigenvalue as the detection statistic, subsequently devising a matrix CFAR detector based on this dual-channel maximum eigenvalue suitable for practical scenarios. In addition, the detector is verified to achieve better practical detection performance with the measured sea clutter data.