Moving Target Detection for FDA Radar With Array Errors in Mainlobe Jamming Scenarios

Abstract

Mainlobe jamming posesa significant threat to victim radar system. In this article, the problem of detecting moving target in the simultaneous presence of mainlobe smart jamming and array gain and phase (GP) errors is addressed in a frequency diverse array (FDA) radar. At the modeling stage, we assume that the elements of FDA radar are partially calibrated, where the array errors are modeled as GP inconsistency. On this basis, we propose a systematic scheme termed as joint jamming suppression and target detection (JJSTD), where the jamming-plus-noise covariance matrix is recovered to achieve precise removal of the jamming. To further enhance the detection performance of the moving target, we resort to the generalized Radon Fourier transform to accumulate target energy. Note that the proposed JJSTD requires neither nonlinear optimizations nor any prior knowledge regarding the target and mainlobe jamming. Numerical examples demonstrate the robustness of the proposed method against the array GP errors and mainlobe jamming. Moreover, it achieves desired outcomes even in the extreme scenario with array element failures and high jamming-to-signal ratio.