Radar Waveform Sequence Design for PSL Optimization via Iterative Neural Network

In radar systems, high-resolution waveforms with favorable correlation properties are preferred. This letter addresses the challenge of designing unimodular radar waveform sets with low peak sidelobe level (PSL) in autocorrelation function (ACF). In contrast to conventional methods, this approach does not attempt to transform a non-convex problem into a convex one through relaxation. Inspired by neural network optimization techniques, an iterative neural network structure for minimizing PSL is proposed in this letter. By employing the Mellowmax operation and incorporating an additional penalty term into the loss function, the optimized ACF with low PSL is obtained. Corresponding simulation experiments demonstrate that our method achieves a superior PSL value of 2-3 dB lower than the state-of-the-art method.