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 Two-Dimensional Direction of Arrival (DOA) Estimation for Rectangular Array via Compressive Sensing Trilinear Model

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Huaxin Yu ◽  
Xiaofeng Qiu ◽  
Xiaofei Zhang ◽  
Chenghua Wang ◽  
Gang Yang
Keyword(s):  
Compressive Sensing ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Estimation Performance ◽  
Trilinear Model ◽  
2D Doa Estimation

We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.

scholarly journals A single triangular SS-EMVS aided high-accuracy DOA estimation using a multi-scale L-shaped sparse array

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jin Ding ◽  
Minglei Yang ◽  
Baixiao Chen ◽  
Xin Yuan
Keyword(s):  
Mutual Coupling ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Cross Product ◽  
High Accuracy ◽  
Sparse Array ◽  
Array Configuration ◽  
Multi Scale ◽  
Half Wavelength

Abstract We propose a new array configuration composed of multi-scale scalar arrays and a single triangular spatially spread electromagnetic-vector-sensor (SS-EMVS) for high-accuracy two-dimensional (2D) direction-of-arrival (DOA) estimation. Two scalar arrays are placed along x-axis and y-axis, respectively, each array consists of two uniform linear arrays (ULAs), and these two ULAs have different inter-element spacings. In this manner, these two scalar arrays form a multi-scale L-shaped array. The two arms of this L-shaped scalar array are connected by a six-component SS-EMVS, which is composed of a spatially spread dipole-triad plus a spatially spread loop-triad. All the inter-element spacings in our proposed array can be larger than a half-wavelength of the incident source, thus to form a sparse array to mitigate the mutual coupling across antennas. In the proposed DOA estimation algorithm, we perform the vector-cross-product algorithm to the SS-EMVS to obtain a set of low-accuracy but unambiguous direction cosine estimation as a reference; we then impose estimation of signal parameters via rotation invariant technique (ESPRIT) algorithm to the two scalar arrays to get two sets of high-accuracy but cyclically ambiguous direction cosine estimations. Finally, the coarse estimation is used to disambiguate the fine but ambiguous estimations progressively and therefore a multiple-order disambiguation algorithm is developed. The proposed array enjoys the superiority of low redundancy and low mutual coupling. Moreover, the thresholds of the inter-sensor spacings utilized in the proposed array are also analyzed. Simulation results validate the performance of the proposed array geometry.

scholarly journals Computationally Efficient Ambiguity-Free Two-Dimensional DOA Estimation Method for Coprime Planar Array: RD-Root-MUSIC Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Luo Chen ◽  
Changbo Ye ◽  
Baobao Li
Keyword(s):  
Degrees Of Freedom ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Music Algorithm ◽  
Estimation Algorithms ◽  
Planar Array ◽  
2D Doa Estimation

While the two-dimensional (2D) spectral peak search suffers from expensive computational burden in direction of arrival (DOA) estimation, we propose a reduced-dimensional root-MUSIC (RD-Root-MUSIC) algorithm for 2D DOA estimation with coprime planar array (CPA), which is computationally efficient and ambiguity-free. Different from the conventional 2D DOA estimation algorithms based on subarray decomposition, we exploit the received data of the two subarrays jointly by mapping CPA to the full array of the CPA (FCPA), which contributes to the enhanced degrees of freedom (DOFs) and improved estimation performance. In addition, due to the ambiguity-free characteristic of the FCPA, the extra ambiguity elimination operation can be avoided. Furthermore, we convert the 2D spectral search process into 1D polynomial rooting via reduced-dimension transformation, which substantially reduces the computational complexity while preserving the estimation accuracy. Finally, numerical simulations demonstrate the superiority of the proposed algorithm.

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.

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