scholarly journals 2D-DOD and 2D-DOA Estimation for a Mixture of Circular and Strictly Noncircular Sources Based on L-Shaped MIMO Radar

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2177
Author(s):  
Jiaxiong Fang ◽  
Yonghong Liu ◽  
Yifang Jiang ◽  
Yang Lu ◽  
Zehao Zhang ◽  
...  
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Joint Diagonalization ◽  
Data Vector ◽  
Direction Of Departure ◽  
Input Multiple Output ◽  
Virtual Array ◽  
Cramer Rao Bound

In this paper, a joint diagonalization based two dimensional (2D) direction of departure (DOD) and 2D direction of arrival (DOA) estimation method for a mixture of circular and strictly noncircular (NC) sources is proposed based on an L-shaped bistatic multiple input multiple output (MIMO) radar. By making full use of the L-shaped MIMO array structure to obtain an extended virtual array at the receive array, we first combine the received data vector and its conjugated counterpart to construct a new data vector, and then an estimating signal parameter via rotational invariance techniques (ESPRIT)-like method is adopted to estimate the DODs and DOAs by joint diagonalization of the NC-based direction matrices, which can automatically pair the four dimensional (4D) angle parameters and solve the angle ambiguity problem with common one-dimensional (1D) DODs and DOAs. In addition, the asymptotic performance of the proposed algorithm is analyzed and the closed-form stochastic Cramer–Rao bound (CRB) expression is derived. As demonstrated by simulation results, the proposed algorithm has outperformed the existing one, with a result close to the theoretical benchmark.

scholarly journals Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2788 ◽  
Author(s):  
Yuehao Guo ◽  
Xianpeng Wang ◽  
Wensi Wang ◽  
Mengxing Huang ◽  
Chong Shen ◽  
...  
Keyword(s):  
Mutual Coupling ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Estimation Performance ◽  
Direction Of Departure ◽  
Value Decomposition

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.

scholarly journals A MIMO Radar-Based DOA Estimation Structure Using Compressive Measurements

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4706 ◽  
Author(s):  
Tao Chen ◽  
Jian Yang ◽  
Muran Guo
Keyword(s):  
Antenna Arrays ◽  
Multiple Input Multiple Output ◽  
Information Criterion ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Optimization Approach ◽  
Measurement Matrix ◽  
Estimation Performance ◽  
Input Multiple Output ◽  
Cramer Rao Bound

In this paper, we propose a novel direction-of-arrival (DOA) estimation structure based on multiple-input multiple-output (MIMO) radar with colocated antennas, referred to as compressive measurement-based MIMO (CM-MIMO) radar, where the compressive sensing (CS) is employed to reduce the number of channels. Therefore, the system complexity and the computational burden are effectively reduced. It is noted that CS is used after the matched filters and that a measurement matrix with less rows than columns is multiplied with the received signals. As a result, the configurations of the transmit and receive antenna arrays are not affected by the CS and can be determined according to the practical requirements. To study the estimation performance, the Cramér–Rao bound (CRB) with respect to the DOAs of the proposed CM-MIMO radar is analyzed in this paper. The derived CRB expression is also suitable for the conventional MIMO radar by setting the measurement matrix as an identity matrix. Moreover, the CRB expression can work in the under-determined case, since the sum-difference coarray structure is considered. However, the random measurement matrix leads to high information loss, thus compromising the estimation performance. To overcome this problem, we consider that the a prior probability distribution of the DOAs associated with the targets can be obtained in many scenarios and an optimization approach for the measurement matrix is proposed in this paper, where the maximum mutual information criterion is adopted. The superiority of the proposed structure is validated by numerical simulations.

Angle Estimation for Bistatic MIMO Radar with Unknown Mutual Coupling Based on Improved Propagator Method

2014 ◽  
Vol 513-517 ◽  
pp. 3029-3033 ◽  
Author(s):  
Jian Feng Li ◽  
Wei Yang Chen ◽  
Xiao Fei Zhang
Keyword(s):  
Mutual Coupling ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Rotational Invariance ◽  
Angle Estimation ◽  
Direction Of Departure ◽  
Input Multiple Output ◽  
Joint Direction

In this paper, joint direction of departure (DOD) and direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied. An improved propagator calculation method is proposed to overcome the performance degradation problem when signal to noise ratio (SNR) is low. Thereafter, according to the Toeplitz structure of the mutual coupling matrix, the rotational invariance can be extracted for the angle estimation regardless of the mutual coupling from the augmented propagator matrix. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and conventional PM-like method, and angles are automatically paired. The simulation results verify the effectiveness of the algorithm.

scholarly journals Thinned Virtual Array for Cramer Rao Bound Optimization in MIMO Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xia Li ◽  
Buhong Wang
Keyword(s):  
Performance Indicator ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Direction Finding ◽  
Mimo Radar ◽  
Combinatorial Problems ◽  
Array Configuration ◽  
Virtual Array ◽  
Array Aperture ◽  
Cramer Rao Bound

By transmitting multiple independent waveforms at the transmit side and processing echoes of spatial targets at the receive side, Multiple Input Multiple Output (MIMO) radar enjoys virtual array aperture expansion and more degree of freedom (DOF), both of which favors the application of direction finding or estimation of direction of arrival (DOA). The expanded virtual aperture provides higher angular resolution which also promotes the precision of DOA estimation, and the extra DOF brought by waveform diversity can be leveraged to focus energy in certain spatial region for better direction-finding capacity. However, beamspace methods which match certain beampatterns suffer from deteriorated performance and complexity in implementation, and the advantage of virtual array aperture is limited by its virtual element redundancy. As an important performance indicator of DOA estimation, Cramer–Rao Bound (CRB) is closely connected to the array configuration of the system. To reduce the complexity of the system and improve CRB performance at the same time, in this paper, the virtual array of MIMO radar is designed directly by selecting outputs from matched filters at the receive side. For the sake of fair comparison, both scenarios with and without priori directions are considered to obtain optimized virtual array configuration, respectively. The original combinatorial problems are approximated by sequential convex approximations methods which produce solutions with efficiency. Numerical results demonstrate that the proposed method can provide thinned virtual arrays with excellent CRB performance.

Conjugate ESPRIT for MIMO Radar without Using Non-Circular Signals

2014 ◽  
Vol 513-517 ◽  
pp. 3850-3854
Author(s):  
Jian Feng Li ◽  
Wei Yang Chen ◽  
Xiao Fei Zhang
Keyword(s):  
Data Model ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Rotational Invariance ◽  
Joint Estimation ◽  
Signal Parameters ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Virtual Array

Without using non-circular signals, conjugate estimation of signal parameters via rotational invariance technique (ESPRIT) for joint estimation of direction of departure (DOD) and direction of arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar is proposed. The characteristics of the Vandermonde-like matrix are employed to expand the virtual array of MIMO radar. Then the rotational invariance in the signal subspace is exploited to get the automatically paired estimations of angles. The proposed algorithm works with the same data model as that of ESPRIT, and has better angle estimation performance and can detect more targets than ESPRIT. Simulation results verify the usefulness of our approach.

Parameter Estimation and Cramer-Rao Bound in Wideband Bistatic MIMO Radar System

2015 ◽  
Vol 713-715 ◽  
pp. 651-655 ◽  
Author(s):  
Li Li
Keyword(s):  
Parameter Estimation ◽  
Fractional Fourier Transform ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Radar System ◽  
Novel Approach ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Cramer Rao Bound

The problem of target localization and parameter estimation in wideband bistatic Multiple-Input Multiple-Output (MIMO) radar system is considered. In this paper, we use a novel approach to estimate Doppler stretch and time delay in fractional Fourier transform (FRFT) domain. We also develop two sub-array models to accurately estimate the direction-of-departure (DOD) and the direction-of-arrival (DOA). Furthermore, the Cramér-Rao bound for target parameter estimation is derived and computed in closed form. Parameter estimation performances are evaluated and studied theoretically and via simulations

scholarly journals Joint DOA and DOD Estimation Based on Tensor Subspace with Partially Calibrated Bistatic MIMO Radar

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Junxiang Wang ◽  
Ping Huang ◽  
Dingjie Xu
Keyword(s):  
Multiple Input Multiple Output ◽  
Closed Form Solution ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Form Solution ◽  
Direction Of Departure ◽  
Input Multiple Output ◽  
Tensor Subspace ◽  
Value Decomposition

A joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation algorithm based on tensor subspace approach for partially calibrated bistatic multiple-input multiple-output (MIMO) radar is proposed. By exploiting the multidimensional structure of the received data, a third-order measurement tensor is constructed. Consequently, the tensor-based signal subspace is achieved using the higher-order singular value decomposition (HOSVD). To achieve accurate DOA estimation with partially calibrated array, a closed-form solution is provided to estimate the gain-phase uncertainties of the transmit and receive arrays by modeling the imperfections of the arrays. Simulation results demonstrate the effectiveness of the proposed calibration algorithm.

scholarly journals Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

2021 ◽  
Vol 13 (15) ◽  
pp. 2964
Author(s):  
Fangqing Wen ◽  
Junpeng Shi ◽  
Xinhai Wang ◽  
Lin Wang
Keyword(s):  
Numerical Study ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Phase Errors ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Angle Pair ◽  
Parallel Factor ◽  
Parafac Decomposition

Ideal transmitting and receiving (Tx/Rx) array response is always desirable in multiple-input multiple-output (MIMO) radar. In practice, nevertheless, Tx/Rx arrays may be susceptible to unknown gain-phase errors (GPE) and yield seriously decreased positioning accuracy. This paper focuses on the direction-of-departure (DOD) and direction-of-arrival (DOA) problem in bistatic MIMO radar with unknown gain-phase errors (GPE). A novel parallel factor (PARAFAC) estimator is proposed. The factor matrices containing DOD and DOA are firstly obtained via PARAFAC decomposition. One DOD-DOA pair estimation is then accomplished from the spectrum searching. Thereafter, the remainder DOD and DOA are achieved by the least squares technique with the previous estimated angle pair. The proposed estimator is analyzed in detail. It only requires one instrumental Tx/Rx sensor, and it outperforms the state-of-the-art algorithms. Numerical simulations verify the theoretical advantages.

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.

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