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

• Take residual network to design waveform for MIMO radar transmit beampattern and get a better performance. • The proposed methods can solve the problem directly and don't need to synthesis waveform covariance matrix. • The proposed methods can also suppress cross-correlation sidelobes while keep a good performance on beampattern matching. • The proposed methods with residual network provide some new ideas for the constant modulus waveform design in MIMO radar. 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. [ABSTRACT FROM AUTHOR]