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

Abstract

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

Keywords

• Waveform design
• autocorrelation
• cross-correlation
• peak sidelobe levels
• deep learning
• neural networks