Research On Target Parameter Estimation With Frequency Diverse Array MIMO Radar Based On Sparse Recover

Frequency diverse array（FDA）is a radar of emerging technology,which forms a two-dimensional beam pattern related to angle-range by using linearly increasing carrier frequencies between transmitting antennas.Compared with the phased array（PA）radar,the FDA radar adds the degree of freedom at the transmitter,so it has great application value in the field of target localization.The FDA-MIMO radar,which combines FDA radar and multiple input multiple output（MIMO）technology,not only has the range-dependent beam of FDA radar,but also has the high degree of freedom and high spatial resolution of MIMO radar.At present,the FDA-MIMO radar parameter estimation method based on the subspace is limited by nonideal scenarios such as low signal-to-noise ratio,scarce snapshots,and related signals.In recent years,the rapid development of sparse signal recovery（SSR）technology provides a new perspective for solving above problem.Different from the subspace-based algorithms,the sparse signal recovery estimation algorithms mainly estimate target parameters by constructing a sparse signal model and reconstructing the spatial spectrum,and demonstrate excellent adaptability to the aforementioned imperfect scenarios.In this paper,the sparse recovery theory is utilized to study the direction of arrival（DOA）and range parameter estimation of monostatic FDA-MIMO radar.Firstly,the antenna structure and beam-pattern of FDA are introduced.Specifically,according to the beam-pattern characteristics of PA and FDA,we analyzed the angle-range correlation of the FDA beam-pattern in detail.Then,the basic concepts of sparse recovery theory are introduced systematically.Moreover,we derive two typical algorithms based on the subspace theory and verified their effectiveness through simulation experiments.A sparse recovery algorithm based on a double-pulse FDA-MIMO radar is proposed to overcome the limitations of the FDA-MIMO radar which involves he low accuracy and limited application scenarios.Firstly,the DOA estimates of targets are calculated by transmitting a pulse with zero frequency increment and employing the iterative grid refinement（IGR）₁l-SVD method.After obtaining DOA estimates,the range estimates of targets are achieved by utilizing a pulse with a nonzero frequency increment.The numerical simulations prove that the proposed algorithm achieves high-precision DOA and range estimation under low signal-to-noise ratio（SNR）or scarce snapshots.Finally,an effective off-grid sparse Bayesian learning（SBL）method is proposed for DOA and range estimation in off-grid situations.First of all,the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm,which,subsequently,are utilized to obtain the paired rough range estimates.Furthermore,a discrete grid is constructed by the rough DOA and range estimates,and the 2D-SBL model is proposed to optimize the rough DOA and range estimates.Moreover,the expectation-maximization（EM）method is employed to update the grid points iteratively to further eliminate the errors caused by the off-grid model.Finally,theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method.