New Optimization Method Based on Neural Networks for Designing Radar Waveforms With Good Correlation Properties

Owing to advances in the overall performance and anti-interception capability of radars, designs radar waveforms with good correlation properties have been a concern for researchers. In this paper, we propose novel method based on convolutional neural networks (CNNs) designing single or multiple unimodular sequences auto- cross-correlation weighted properties. The framework sequence optimization is constructed using group convolution identity mapping, three different loss functions are presented objectives. To illustrate proposed method, present numerous examples, including design low autocorrelation sidelobes specified lag interval set Moreover, an analysis simulations shows that designed through our demonstrate better than classic algorithms.