MIMO Radar Waveform Design for Range-ISL Optimization via Iterative Deep Unfolding Network

Multiple-input multiple-output (MIMO) radar unimodular waveform design with range-integrated sidelobe level (ISL) optimization is a key technology in remote sensing. Due to the nonconvex quartic objective function and constant modulus constraint (CMC), the problem is NP-hard and nonconvex. Existing methods mainly include relaxation methods or nonrelaxation methods with huge computational costs. We notice that complex circle manifold (CCM) naturally satisfies the CMC. By projecting onto the CCM, the problem is transformed into an unconstrained minimization problem that can be addressed using the Riemannian gradient descent (RGD) algorithm. Furthermore, we notice that the RGD algorithm can be unfolded into a deep-learning model. Hence, a computationally efficient method without relaxation, iterative deep unfolding network (IDUN), is proposed. First, this problem is converted into an unconstrained fourth-order polynomial minimization problem on the CCM. Then, by unfolding the RGD algorithm as the network layer, IDUN is developed by adaptively learning the step sizes. Compared with existing methods, the proposed method has superior performance and less computational cost.