| The multiple-input multiple-output(MIMO)radar system is a new type of radar system,which has more degrees of freedom(DOF),stronger anti-jamming and noise,and better angle estimation ability than phased array radar,so researchers have been widely concerned about it.According to the architecture of transmit and receive arrays,MIMO radar can be divided into two categories: statistical MIMO radar and colocated MIMO radar.Focusing on the direction of arrival(DOA)estimation for colocated MIMO radar,this dissertation studies these problems in combination with the spatial filter design and optimization theory.The main contributions and innovations are shown as follows:(1)Aiming at the problem of a large number of virtual elements and large receive data dimension of monostatic MIMO radar,a real-valued ESPRIT angle measurement algorithm based on reduced-dimension beamspace is proposed.First,the high-dimensional MIMO radar received data is converted to low-dimensional data through the conversion matrix to eliminate the redundancy in the data.Then,the Discrete Fourier Transform(DFT)beamspace matrix is utilized to change the beamspace of the low-dimensional data and construct the real-value rotation invariance equation to estimate the DOA of the target.The simulation results show that the proposed algorithm has high estimation accuracy and low computational complexity under the conditions of low snapshot and low signal-to-noise ratio(SNR).(2)In the monostatic MIMO radar system model,the traditional DFT beamspace matrix cannot flexibly control the mainlobe width and sidelobe level.In the monostatic MIMO radar system model,the traditional DFT beamspace matrix cannot flexibly control the mainlobe width and sidelobe level,so it has a low mainlobe-to-sidelobe ratio(MSR).A new spatial filter is designed using the convex optimization technique to solve this problem.At the same time,in order to ensure that the converted noise is still Gaussian white noise,the beamspace data is pre-whitened.Finally,a unitary ESPRIT angle measurement algorithm of reduced dimension beamspace is proposed by applying unitary transformation technology.In addition,the cramer-rao bound(CRB)of array element space and reduced dimension beamspace are also analyzed.Simulation results verify the effectiveness of the proposed algorithm.(3)In the monostatic MIMO radar system,the received data are de-redundant and spatially filtered and combined with conformal transformation,a real polynomial rooting angle measurement algorithm based on beamspace is proposed.In order to avoid expensive spectral search,conformal transformation is used to convert complex-valued variables into real-valued variables,and the extreme points of the MUSIC spectrum are determined by the polynomial rooting technique.Since the proposed algorithm is based on real-valued processing and does not need spectral search,it has low computational complexity.In addition,the simulation results show that the proposed algorithm has lower computational complexity and better angle estimation accuracy than the traditional complex-valued polynomial root algorithm.(4)In the phased array radar or MIMO radar system,combining spatial filtering processing and sparse Bayesian learning(SBL)algorithm,an off-grid real-valued SBL angle measurement algorithm is proposed.Firstly,the received data is filtered by DFT spatial filter to obtain real-valued beamspace data.Then,singular value decomposition is performed to reduce the data dimension further and extract the signal subspace.Finally,the DOA of the target is estimated with SBL algorithm.The simulation results show that the proposed algorithm has better angle estimation performance and robustness for rough grids than the traditional SBL algorithm in array element space.(5)The sum difference co-array(SDC)generated by the coprime planar array(CPPA)has holes,which makes it impossible to fully utilize the virtual aperture.To solve this issue,a two-dimensional(2-D)directional of arrival(DOA)estimation algorithm with CPPA via matrix completion and sparse matrix recovery is proposed.To accurately complete the missing elements in the SDC,we construct an optimization problem based on the truncated nuclear norm regularization(TNNR)by constraining the conjugate flip symmetry property of the virtual array and the noise term.Then,the complex-valued fast iterative shrinkage threshold algorithm(CV-FISTA)is derived to recover the sparse matrix and estimate the 2-D DOA.In addition,the proposed algorithm avoids Kronecker product operations between dictionary matrices,thereby reducing the computational burden of traditional vector form sparse recovery algorithms.Therefore,the proposed algorithm can achieve a larger virtual aperture and lower computational complexity.The simulation results show that the proposed algorithm has better angle estimation performance and lower computational burden under certain conditions.(6)Compared with the traditional MIMO radar,the coprime MIMO radar has a larger virtual aperture,thus obtaining more DOF and improving the number of target detectors.The traditional coprime MIMO radar transmits orthogonal waveforms at the expense of coherent gain,thus reducing the detection performance.In order to solve the above problems,a coprime MIMO radar based on transmit energy focusing(TEF)is proposed by combining the coprime MIMO radar and TEF technology.Compared with the traditional coprime MIMO radar,the proposed coprime MIMO radar based on the TEF technique can focus the transmitted energy without sacrificing the virtual aperture of the array.In order to obtain more DOF,the beamspace matrix with a unique structure is designed to ensure that the sum and difference coarray(SDC)of the vectorized sampling covariance matrix has the maximum consecutive lags.Since the transmitted energy is focused on the desired spatial sector,the proposed model can improve the SNR gain of the desired sector.Compared with the traditional coprime MIMO radar,the simulation results show the superior performance of the proposed model.(7)In order to reduce the power consumption of radio frequency(RF)components of MIMO radar and obtain the desired transmit beam pattern,a reconfigurable sparse MIMO radar based on the TEF technique is proposed for DOA estimation.The transmitted beam pattern is designed using the template matching constraint,and the row sparsity of the transmit beamspace matrix is controlled by the weighted group sparse norm.Then,the alternating direction method of multipliers(ADMM)framework is used to solve the above non-convex model problem to obtain the transmit beam space matrix with row sparsity.In addition,the proximal gradient(PG)descent algorithm is derived to solve the nondifferentiability problem of the sparse norm of the weighted group.Simulation results show that compared with traditional MIMO radar,the reconfigurable sparse MIMO radar based on the TEF technique has better DOA estimation performance,lower computational complexity,and less hardware resource consumption. |
