scholarly journals Two-Dimensional DOA Estimation of MIMO Radar Coherent Source Based on Toeplitz Matrix Set Reconstruction

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fei Zhang ◽  
Aisuo Jin ◽  
Yin Hu
Keyword(s):  
Toeplitz Matrix ◽  
Full Rank ◽  
Complete Information ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Dimensional Reconstruction ◽  
Esprit Algorithm ◽  
Coherent Source

In order to realize the high-precision direction of arrival (DOA) estimation of the coherent source of two-dimensional multiple-input and multiple-output (MIMO) radar, a solution is given by combining Toeplitz matrix set reconstruction. MIMO radar obtains a larger aperture with fewer arrays. Traditional two-dimensional reconstruction Toeplitz-like algorithms use part of the information in the construction of two correlation matrices or covariance matrices to construct the Toeplitz matrix when performing two-dimensional coherent source DOA estimation, which makes the information utilization incomplete and requires additional denoising processing. To solve the above problems, this paper proposes an improved Toeplitz matrix set reconstruction algorithm based on the two-dimensional reconstruction Toeplitz class algorithm. The complete array element receiving signal vector is used to construct two Toeplitz matrix sets containing complete information, and then their conjugate transposes. Multiply and sum to correct the matrix to obtain a full-rank matrix, so as to achieve the purpose of decoherence and combine the traditional ESPRIT algorithm to perform two one-dimensional reconstruction processing through rotation invariance and then perform angle matching to achieve two-dimensional coherent signal angle estimation, while avoiding additional denoising processing. Finally, the simulation results of the cross array and the L-shaped array verify the effectiveness of the algorithm in this paper and further extend it to the two-dimensional MIMO radar array model and compare it with the traditional ESPRIT-like algorithm and the REC-FBSS-ESPRIT algorithm. In comparison, the algorithm in this paper has better performance under the conditions of successful resolution, DOA estimation accuracy, and low signal-to-noise ratio.

scholarly journals Transmit and Receive Array Structure Design of Two-Dimensional hybrid Phased-MIMO Radar based on Nested Array

2018 ◽  
Vol 232 ◽  
pp. 02052
Author(s):  
Tianhao Cheng ◽  
Buhong Wang ◽  
Qiaoge Liu ◽  
Jiwei Tian
Keyword(s):  
Design Method ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Structure Design ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Simulation Experiments ◽  
Virtual Array ◽  
Array Structure ◽  
Receive Array

In order to reduce the loss of Degree of Freedom (DOF) brought by the transmit subarray splitting of two-dimensional hybrid phased-MIMO radar, this paper presents a design method of transmitting and receiving array based on nested array structure. Firstly, a two-dimensional hybrid phased-MIMO radar transmitting array based on one-dimensional nested array is presented. On this basis, the receiving end is set as a nested array, and finally a virtual array and difference coarray are formed to expand the number of virtual array elements. The expansion increases the DOF of arrays while preserving the advantages of hybrid phased-MIMO radars. Simulation experiments show that compared with the traditional and coprime hybrid phased-MIMO radar, the proposed method can effectively improve the array DOF and Direction-of-Arrival (DOA) estimation accuracy.

scholarly journals 2-D DOA Estimation Method Based on Coprime Array MIMO Radar

2021 ◽  
Author(s):  
Fei Zhang ◽  
Zijing Zhang ◽  
Aisuo Jin ◽  
Chuantang Ji ◽  
Yi Wang
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Degree Of Freedom ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Multiple Sources ◽  
Sparse Array ◽  
Coprime Array

Abstract Aiming at the problem that traditional Direction of Arrival (DOA) estimation methods cannot handle multiple sources with high accuracy while increasing the degree of freedom, a new method of 2-D DOA estimation based on coprime array MIMO Radar (SA-MIMO-CA). Frist of all, in order to ensure the accuracy of multi-source estimation when the number of elements is finite, a new coprime array model based on MIMO (MIMO-CA) is proposed. The array model uses a special irregular array as the transmitting array and a uniform linear array as the receiving array. Besides, in order to reduce complexity and improve the accuracy of two-dimensional DOA estimation, a new two-dimensional DOA estimation method based on sparse array is proposed. This method uses the sparse array topology of virtual array elements to analyze a larger number of information sources, and combines the compressed sensing method to process the sparse array, and obtains a larger array aperture with a smaller number of array elements, and improves the resolution of the azimuth angle. This method improves the DOA estimation accuracy and reduces the complexity. Finally, experiments verify the effectiveness and reliability of the SA-MIMO-CA method in improving the degree of freedom of the array, reducing the complexity, and improving the accuracy of the DOA.

scholarly journals Multiple RFI Sources Location Method Combining Two-Dimensional ESPRIT DOA Estimation and Particle Swarm Optimization for Spaceborne SAR

2021 ◽  
Vol 13 (6) ◽  
pp. 1207
Author(s):  
Junfei Yu ◽  
Jingwen Li ◽  
Bing Sun ◽  
Yuming Jiang ◽  
Liying Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Geographic Location ◽  
Doa Estimation ◽  
Pso Algorithm ◽  
Two Dimensional ◽  
Swarm Optimization ◽  
Location Method ◽  
Esprit Algorithm ◽  
To Receive

Synthetic aperture radar (SAR) systems are susceptible to radio frequency interference (RFI). The existence of RFI will cause serious degradation of SAR image quality and a huge risk of target misjudgment, which makes the research on RFI suppression methods receive widespread attention. Since the location of the RFI source is one of the most vital information for achieving RFI spatial filtering, this paper presents a novel location method of multiple independent RFI sources based on direction-of-arrival (DOA) estimation and the non-convex optimization algorithm. It deploys an L-shaped multi-channel array on the SAR system to receive echo signals, and utilizes the two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm to estimate the positional relationship between the RFI source and the SAR system, ultimately combines the DOA estimation results of multiple azimuth time to calculate the geographic location of RFI sources through the particle swarm optimization (PSO) algorithm. Results on simulation experiments prove the effectiveness of the proposed method.

scholarly journals A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 827 ◽  
Author(s):  
Feilong Liu ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Liangtian Wan ◽  
Huafei Wang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
Phase Ambiguity ◽  
Steering Vector ◽  
Input Multiple Output ◽  
Joint Direction ◽  
Esprit Algorithm ◽  
Cramer Rao Bound

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér–Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results.

scholarly journals An Improved Two-Dimensional Direction-Of-Arrival Estimation Algorithm for L-Shaped Nested Arrays with Small Sample Sizes

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2176 ◽  
Author(s):  
Xiaofeng Gao ◽  
Xinhong Hao ◽  
Ping Li ◽  
Guolin Li
Keyword(s):  
Correlation Matrix ◽  
Degrees Of Freedom ◽  
Full Rank ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Covariance Matrices ◽  
Small Sample ◽  
Two Dimensional ◽  
Nested Arrays

In this paper, an improved two-dimensional (2-D) direction of arrival (DOA) estimation algorithm for L-shaped nested arrays is proposed. Unlike the approach for a classical nested array, which use the auto-correlation matrix (ACM) to increase the degrees of freedom (DOF), we utilize the cross-correlation matrix (CCM) of different sub-arrays to generate two long consecutive virtual arrays. These acquire a large number of DOF without redundant elements and eliminate noise effects. Furthermore, we reconstruct the CCM based on the singular value decomposition (SVD) operation in order to reduce the perturbation of noise for small numbers of samples. To cope with the matrix rank deficiency of the virtual arrays, we construct the full rank equivalent covariance matrices by using the output and its conjugate vector of virtual arrays. The unitary estimation of signal parameters via rotational invariance technique (ESPRIT) is then performed on the covariance matrices to obtain the DOA of incident signals with low computational complexity. Finally, angle pairing is achieved by deriving the equivalent signal vector of the virtual arrays using the estimated angles. Numerical simulation results show that the proposed algorithm not only provides more accurate 2-D DOA estimation performance with low complexity, but also achieves angle estimation for small numbers of samples compared to existing similar methods.

scholarly journals Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Guimei Zheng ◽  
Jun Tang
Keyword(s):  
Electromagnetic Field ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Field Vector ◽  
Cross Product ◽  
Mimo Radar ◽  
Two Dimensional ◽  
Array Configuration ◽  
2D Doa Estimation

We study two-dimensional direction of arrival (2D-DOA) estimation problem of monostatic MIMO radar with the receiving array which consists of electromagnetic vector sensors (EMVSs). The proposed angle estimation algorithm can be applied to the arbitrary and unknown array configuration, which can be summarized as follows. Firstly, EMVSs in the receiver of a monostatic MIMO radar are used to measure all six electromagnetic-field components of an incident wavefield. The vector sensor array with the six unknown electromagnetic-field components is divided into six spatially identical subarrays. Secondly, ESPRIT is utilized to estimate the rotational invariant factors (RIFs). Parts of the RIFs are picked up to restore the source’s electromagnetic-field vector. Last, a vector cross product operation is performed between electric field and magnetic field to obtain the Pointing vector, which can offer the 2D-DOA estimation. Prior knowledge of array elements’ positions and angle searching procedure are not necessary for the proposed 2D-DOA estimation method. Simulation results prove the validity of the proposed method.

scholarly journals Off-Grid DoA Estimation on Non-Uniform Linear Array Using Constrained Hermitian Matrix

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5775
Author(s):  
Hyeonjin Chung ◽  
Jeungmin Joo ◽  
Sunwoo Kim
Keyword(s):  
Toeplitz Matrix ◽  
Linear Array ◽  
Hermitian Matrix ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Uniform Linear Array ◽  
Atomic Norm ◽  
Array Structures ◽  
Simulation Results

In this paper, an off-grid direction-of-arrival (DoA) estimation algorithm which can work on a non-uniform linear array (NULA) is proposed. The original semidefinite programming (SDP) representation of the atomic norm exploits a summation of one-rank matrices constructed by atoms, where the summation of one-rank matrices equals a Hermitian Toeplitz matrix when using the uniform linear array (ULA). On the other hand, when the antennas in the array are placed arbitrarily, the summation of one-rank matrices is a Hermitian matrix whose diagonal elements are equivalent. Motivated by this property, the proposed algorithm replaces the Hermitian Toeplitz matrix in the original SDP with the constrained Hermitian matrix. Additionally, when the antennas are placed symmetrically, the performance can be enforced by adding extra constraints to the Hermitian matrix. The simulation results show that the proposed algorithm achieves higher estimation accuracy and resolution than other algorithms on both array structures; i.e., the arbitrary array and the symmetric array.

scholarly journals Two-Dimensional Direction-of-Arrival Estimation for Trilinear Decomposition-Based Monostatic Cross MIMO Radar

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Fangqing Wen ◽  
Gong Zhang
Keyword(s):  
Direction Of Arrival ◽  
Doa Estimation ◽  
Low Complexity ◽  
Mimo Radar ◽  
Eigenvalue Decomposition ◽  
Two Dimensional ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Trilinear Decomposition ◽  
Signal Covariance Matrix

A low complexity monostatic cross multiple-in multiple-out (MIMO) radar scheme is proposed in this paper. The minimum-redundancy linear array (MRLA) is introduced in the cross radar to improve the efficiency of the array elements. The two-dimensional direction-of-arrival (DOA) estimation problem links to the trilinear model, which automatically pairs the estimated two-dimensional angles, requiring neither eigenvalue decomposition of received signal covariance matrix nor spectral peak searching. The proposed scheme performs better than the uniform linear arrays (ULA) configuration under the same conditions, and the proposed algorithm has less computational complexity than that of multiple signal classification (MUSIC) algorithm. Simulation results show the effectiveness of our scheme.

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