Low-Complexity Subarray-Based Adaptive Detection for Multichannel Application in Inhomogeneous Clutter Environments

Multichannel adaptive detection (MAD) can achieve better performance compared with the constant false alarm rate (CFAR) methods in target detection in inhomogeneous clutter. However, its application still faces many challenges, such as the lack of sufficient training samples and huge computational costs. In this letter, a low-complexity reduced-dimension MAD (RD-MAD) scheme in an inhomogeneous clutter environment is proposed based on arbitrary subarray synthesis. By this scheme, we derive the RD generalized likelihood ratio test (GLRT). The theoretical performance of the proposed method is analyzed, including the CFAR property, RD performance, and computational complexity. Finally, with tri-channel X-band airborne radar real data, the detection performance of the proposed RD-MAD scheme is verified. Compared with the existing detectors, the proposed detector can provide better detection performance in sample-insufficient environments with much lower computational complexity.