Riemannian gradient deep network for joint waveform and filter optimization in MIMO radar against chopping forwarding jamming and clutter

Abstract

With the rise of digital radio frequency memory technology, active deception jamming poses a significant threat to radar systems, especially in detecting targets amid mainlobe jamming and non-Gaussian clutter. Traditional methods like space–time matched filtering struggle in such scenarios. This study introduces the Riemannian gradient deep network (RGDN), a framework for joint optimization of transmit waveforms and receive filters to improve target detection. Unlike conventional signal-to-clutter noise ratio (SCNR) maximization, RGDN leverages information geometry to maximize the Kullback–Leibler Divergence (KLD) between targets and clutter. By modeling non-Gaussian data with a Gaussian mixture distribution and constructing a Riemannian manifold, the framework achieves effective jamming suppression through receive filter term in the loss function, minimizing jamming effects while enhancing target-clutter distinguishability. To address non-convex optimization, Riemannian gradient descent is integrated into a deep network. Numerical experiments show that RGDN achieves superior detection performance compared to SCNR maximization method.