Constant Modulus MIMO Radar Waveform Design via Iterative Optimization Network Method

Constant modulus (CM) waveform design with good auto- and cross correlation properties is the key issue in the multiple-input multiple-output (MIMO) radar systems. The problem is nonconvex and NP-hard, due to the CM constraint and the nonconvex objective function. Most existing methods solve this problem by relaxation (relaxing CM constraint or the objective function) or directly designing phase, which degrades the performance or needs huge computational cost. To address these issues, an unsupervised double iterative optimization network (ION) method is proposed, using the strong nonlinear 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 cross correlation sidelobe (WMCS) with less computational cost.