Constant Modulus MIMO Radar Waveform Design via Iterative Optimization Network Method
Kai Zhong, Jinfeng Hu, Cunhua Pan
Abstract
Constant modulus (CM) waveform design with good auto- and cross- correlation properties is the key issue in the multiple-input multiple-output (MEMO) radar systems. The problem is non-convex and NP-hard, due to the CM constraint and the nonconvex objective function. Most existing methods solve this problem by relaxation (relaxing CM costrint or the objective function) or directly designing phase, which degrade the performance or need huge computational cost. To address these issues, an unsupervised double Iterative Optimization Network (ION) method is proposed, by using the strong non-linear mapping ability of the deep learning network. The outer iteration updates the input waveform, and the inner iteration optimizes the waveform through the residual network. Compared with the existing methods, the proposed method has lower sidelobe in both the weighted maximum autocorrelation sidelobe (WMAS) and the weighted maximum crosscorrelation sidelobe (WMCS) with less computational cost.