Research On MIMO GMTI Adaptive Transmitter STAP Technology

Multi-input multi-output(MIMO)radar is a type of radar that uses multiple transmit antennas and multiple receive antennas.MIMO radar makes full use of the radar's transmit freedom and could form a longer equivalent baseline,that means it have great potential in ground moving target indication(GMTI).Expand the Space-Time Adaptive Processing(STAP)architecture to the MIMO radar system,compared with the traditional Single Input Multiple Output(SIMO)STAP,it has increased Doppler resolution,more reduced and sharper clutter notches.The joint optimization of receive filters and transmit waveforms of MIMO STAP,could make good use of the pre-information of the target and clutters to adaptively optimize the transmitters and receivers,so as to further enhance the detection performance of low-velocity ground moving target.The main work of this paper includes:Chapter ? is the introduction.The research background and research significances of this the topic of this paper are summarized.The development and research status of the MIMO GMTI technology are summarized.The content structure and the chapter arrangement of the whole text are introduced.Chapter ? introduces the basic theory of the forward-squint-looking STAP.The working mode of the forward-squint-looking radar is introduced and echo signal model is constructed.The basic theory of full STAP is summarized,target at the problem of the huge amount of calculation and the lack of independent identical distribution(IID)samples to estimate the clutter covariance matrix,dimension reduction STAP methods are introduced.All the methods are simulated and compared.As the fundamental theory of the whole paper,the output of this chapter could act as the pre-information and precondition of the following two chapters.Chapter ? introduce the joint optimization of receive filters and transmit waveforms of MIMO with fixed pulse-to-pulse waveforms.The joint optimization under energy constrain is discussed.The maximize of output Signal-to-Interference-noise ratio(SINR)is taken as the optimize target,cyclic optimization is used to resolve the non-convex problem.This joint optimization method could effectively reduce the Minimum Detectable Velocity(MDV).Under the multi-target condition,the targets with similar space-time steering vector will have the mutual suppression,a joint optimization method by reconstruct of clutter and noise covariance matrix is introduced,which could effectively deduce the impact of other targets.Aimed at the non-ideal condition of the error in target steering vector,a robust joint optimization method is introduced.Chapter ? expand the the joint optimization of receive filters and transmit waveforms of MIMO with fixed pulse-to-pulse waveforms into variance pulse-to-pulse waveforms mode.The joint optimization under constant-envelope constrain and similarity constrain are discussed.As the optimization problems are non-convex problems,the method of relaxation and randomization,fractional programming and power method-liked iteration are used to solve the optimization under similarity constrain;the method of relaxation and rank-one decomposition,fractional programming and SWORD algorithm are used to solve the optimization problem under constant-envelope constrain.Through simulation experiments,compared with the optimization with fixed pulse-to-pulse waveforms,it is verified that the joint optimization with pulse-to-pulse waveform variations could further improve the detection performance.Under the optimization methods of this paper,after adding constant-envelope constrain and similarity constrain,the target detection performance will not be significantly reduced.Chapter ? is the summary and outlook,all the achievements and the innovation of this paper are summarized,and the prospect of the future work is put forward.