An Ambiguity Function Shaping Method Based on Deep Learning Residual Network
Unimodular waveform design based on ambiguity function(AF)shaping is a crucial technique in radar systems.This problem is formulated as a nonlinear complex quartic optimization problem(NP-hard).The existing meth-ods can be classified into two categories:the first one solves the problem by relaxing the original problem,but inevitably introducing approximation errors;the second one solves the problem directly,but the selection of parameters in this cate-gory is difficult.We notice that deep neural network is a naturally nonlinear system that is highly compatible with that nonlinear problem.Motivated by this,this paper proposes a method based on deep learning residual network for AF shaping without any relaxation or complex parameters selection.The specific steps are as follows:1)The problem is transformed into an unconstrained phase optimization problem;2)The non-convex objective function of the uncon-strained problem is constructed as the loss function of the network;3)The residual network is used to directly optimize the phase of the waveform.The simulation results show that the signal-to-interference ratio(SIR)of the proposed method is improved significantly,and it has better target detection performance.
deep learningambiguity functionresidual networkconstant modulus constraintwaveform design
万方数据
维普
