DOD Estimation Research Articles

Co-Prime MIMO Radar for DOD, DOA, and Doppler Frequency Estimation With Low Complexity

Co-Prime MIMO Radar for DOD, DOA, and Doppler Frequency Estimation With Low Complexity

Joint DOD and DOA Estimation for Bistatic MIMO Radar in the Presence of Unknown Mutual Coupling

Joint DOD and DOA Estimation for Bistatic MIMO Radar in the Presence of Unknown Mutual Coupling

Joint Estimation Method of DOD and DOA of Bistatic Coprime Array MIMO Radar for Coherent Targets Based on Low-Rank Matrix Reconstruction.

Based on low-rank matrix reconstruction theory, this paper proposes a joint DOD and DOA estimation method for coherent targets with bistatic coprime array MIMO radar. Unlike the conventional vectorization, the proposed method processed the coprime array with virtual sensor interpolation, which obtained a uniform linear array to generate the covariance matrix. Then, we reconstructed the Toeplitz matrix and established a matrix optimization recovery model according to the kernel norm minimization theory. Finally, the reduced dimension multiple signal classification algorithm was applied to estimate the angle of the coherent targets, with which the automatic pairing of DOD and DOA could be realized. With the same number of physical sensors, the proposed method expanded the array aperture effectively, so that the degree of freedom and angular resolution could be improved significantly for coherent signals. However, the effectiveness of the method was largely limited by the signal-to-noise ratio. The superiority and effectiveness of the method were proved using simulation experiments.

Improved 2D-MUSIC estimation for low intercept coprime MIMO radar

In electronic warfare, airborne multiple input multiple output (MIMO) radar must not only accurately detect the Angle of moving target, but also ensure the low interception performance of radar. In order to solve this problem, a low interception bistatic MIMO radar with improved coprime array is proposed in this paper. By expanding the coprime array of the bistatic MIMO radar, the array spacing is further expanded to form a sparse array, which reduces the hardware and software cost and improves the anti-jamming capability. In addition, based on the two-dimensional MUSIC algorithm to detect the Angle of the maneuvering target, Taylor expansion method is added to realize the Angle estimation under low SNR. The research results show that, compared with the joint DOD and DOA estimation in a bistatic MIMO radar, the improved method in this paper increases the estimation accuracy, reduces the computational cost of the algorithm, applies to low SNR environment, and ensures low interception performance.

Direction-of-Departure and Direction-of-Arrival Estimation Algorithm Based on Compressive Sensing: Data Fitting

In this paper, a compressive sensing-based data fitting direction-of-departure/direction-of-arrival (DOD/DOA) estimation algorithm is proposed to apply the superior performance of compressive sensing method to the bistatic MIMO sonar systems. The algorithm proposed in this paper optimizes the output data via convex optimization-based sparse recovery, so that it is possible to estimate the DOD and the DOA for each target accurately. In order to minimize the amount of computation, the cost function with constraint condition is implemented in this paper. Furthermore, the constraint condition parameter of the cost function is analytically derived. Through various simulations, it is shown that the superior DOD and DOA estimation performance of the proposed algorithm and that the analytical derivation of the constraint condition parameter is useful for determination of regularization parameter.

Iterative marginal maximum likelihood DOD and DOA estimation for MIMO radar in the presence of SIRP clutter

Abstract The spherically invariant random process (SIRP) clutter model is commonly used in scenarios where the radar clutter cannot be correctly modeled as a Gaussian process. In this short communication, we devise a novel Maximum-Likelihood (ML)-based iterative estimator for direction-of-departure and direction-of-arrival estimation in the Multiple-input multiple-output (MIMO) radar context in the presence of SIRP clutter. The proposed estimator employs a stepwise numerical concentration approach w.r.t. the objective function related to the marginal likelihood of the observation data. Our estimator leads to superior performance, as our simulations show, w.r.t. to the existing likelihood based methods, namely, the conventional, the conditional and the joint likelihood based estimators, and w.r.t. the robust subspace decomposition based methods. Finally, interconnections and comparison between the Iterative Marginal ML Estimator (IMMLE), Iterative Joint ML Estimator (IJMLE) and Iterative Conditional ML Estimator (ICdMLE) are provided.

Fast super-resolution estimation of DOA and DOD in bistatic MIMO Radar with off-grid targets

ABSTRACTIn this paper, we focus on the problem of joint DOA and DOD estimation in Bistatic MIMO Radar using sparse reconstruction method. In traditional ways, we usually convert the 2D parameter estimation problem into 1D parameter estimation problem by Kronecker product which will enlarge the scale of the parameter estimation problem and bring more computational burden. Furthermore, it requires that the targets must fall on the predefined grids. In this paper, a 2D-off-grid model is built which can solve the grid mismatch problem of 2D parameters estimation. Then in order to solve the joint 2D sparse reconstruction problem directly and efficiently, three kinds of fast joint sparse matrix reconstruction methods are proposed which are Joint-2D-OMP algorithm, Joint-2D-SL0 algorithm and Joint-2D-SOONE algorithm. Simulation results demonstrate that our methods not only can improve the 2D parameter estimation accuracy but also reduce the computational complexity compared with the traditional Kronecker Compressed Sensing method.

DOD and DOA estimation in bistatic MIMO radar for nested and coprime array with closed-form DOF

ABSTRACTIt is well known that sparse array can offer better angle resolution than that of uniform linear array (ULA) in the same number of physical sensors. But in bistatic minimum redundancy sparse array multi-input multi-output (MIMO) radar, it cannot offer closed-form degree of freedom (DOF) for the arbitrary number of sensors with direction of departure and direction of arrival estimation. Therefore, this article introduces a nested array and coprime array into sparse array to solve the problem. First, construct no holes difference-coarray by extracting specified covariance matrix elements. Then, transform the difference-coarray into ULA within bistatic MIMO radar through some mathematical operations. As a result, many angle estimation methods for traditional ULA can be applied to the sparse bistatic MIMO array radar. The proposed algorithm offers closed-form DOFs for sparse array and the array aperture is much larger than that of ULA with identical number of sensors. The usefulness of the proposed methods is verified through computer simulations.

Joint DOD and DOA Estimation of High Speed Moving Target in Bistatic MIMO Radar

Joint DOD and DOA Estimation of High Speed Moving Target in Bistatic MIMO Radar

Joint DOA and DOD Estimation in Bistatic MIMO Radar without Estimating the Number of Targets

Existing subspace-based direction finding methods for multiple-input multiple-output (MIMO) radar assume perfect knowledge about the dimension of the signal or noise subspace, which is hard to be established without prior knowledge of the signal environment. In this paper, an efficient method for joint DOA and DOD estimation in bistatic MIMO radar without estimating the number of targets is presented. The proposed method computes an estimate of the noise subspace using the power of R (POR) technique. Then the two-dimensional (2D) direction finding problem is decoupled into two successive one-dimensional (1D) angle estimation problems by employing the rank reduction (RARE) estimator.

Computationally efficient DOD and DOA estimation for bistatic MIMO radar with propagator method

In this article, we consider a computationally efficient direction of departure and direction of arrival estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. The computational loads of the propagator method (PM) can be significantly smaller since the PM does not require any eigenvalue decomposition of the cross correlation matrix and singular value decomposition of the received data. An improved PM algorithm is proposed to obtain automatically paired transmit and receive angle estimations in the MIMO radar. The proposed algorithm has very close angle estimation performance to conventional PM, which has a much higher complexity than our algorithm. For high signal-to-noise ratio, the proposed algorithm has very close angle estimation to estimation of signal parameters via rotational invariance technique algorithm. The variance of the estimation error and Cramér–Rao bound of angle estimation are derived. Simulation results verify the usefulness of our algorithm.