FDA-MIMO radar detecting target embedded in mainlobe deceptive jamming plus Gaussian noise

Abstract

In this paper, we address the challenge of detecting targets embedded in a mainlobe deceptive jamming environment, compounded by Gaussian noise, for the frequency diverse array multiple input multiple output (FDA-MIMO) radar. Unlike conventional MIMO and/or phased-array (PA) radar systems, FDA-MIMO radar leverages additional range information, presenting a promising avenue for mitigating mainlobe deception interference. Our work begins by establishing a waveform-orthogonal FDA-MIMO radar received signal model under Gaussian noise, which encompasses thermal noise, suppression interference, and clutter after secondary range dependence compensation. Subsequently, we formulate the problem of binary hypothesis signal detection in the presence of mainlobe deception interference and training data within a detection scenario. To address this detection problem, we propose two adaptive detectors based on the one-step and two-step general likelihood ratio test (GLRT) criteria, denoted as OGLRT and TGLRT, respectively. Numerical simulation results demonstrate that our proposed detectors exhibit the constant false alarm rate (CFAR) property against the noise covariance matrix. Moreover, the performance of our proposed methods surpasses that of existing subspace detectors, with OGLRT exhibiting the best detection performance. As the frequency offsets for FDA-MIMO radar decrease, the performance of all detectors gradually deteriorates until they no longer retain target detection capabilities. This observation implies that, in the mainlobe deception interference environment considered in this paper, MIMO radar loses its ability to detect targets.