Constant modulus waveform design for MIMO radar transmit beampattern with residual network

Abstract

This paper considers the design of MIMO radar waveform to approximate a desired beampattern while minimizing the cross-correlation sidelobes under the constant modulus constraint. Since the resulting problem is high-dimensional and non-convex (also known as NP-hard), it is extremely difficult to find the global optimization solution through polynomial-time algorithms. A possible methodology is to invoke heuristic iterative optimization algorithms to find an approximation solution by providing as small an beampattern matching error as possible. Recently, we notice that the residual neural network is naturally a nonlinear system, which is very suitable for solving the above problem. In this respect, for the first time, we introduce the residual neural networks to the MIMO radar waveform design for transmit beampattern. More precisely, we formulate two transmit beampattern optimization problems, then convert them into the univariate optimization problems. Finally, we solve them with the designed residual neural network, respectively. Numerical results show that the proposed method can obtain the better beampattern performance over the state-of-the-art methods.