Direction synthesis in DOA estimation for monostatic multiple input multiple output (MIMO) radar based on synthetic impulse and aperture radar (SIAR) and its performance analysis

2008 ◽  
Vol 51 (6) ◽  
pp. 656-673 ◽  
Author(s):  
GuangHui Zhao ◽  
BaiXiao Chen ◽  
ShouPing Zhu
Keyword(s):  
Performance Analysis ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Aperture Radar

MIMO Radar Target Detection Performance Analysis with Unknown Parameters

2011 ◽  
Vol 403-408 ◽  
pp. 182-186
Author(s):  
Wei Wei Liu ◽  
Ning Cao ◽  
Hao Lu ◽  
Ju Rong Hu
Keyword(s):  
Performance Analysis ◽  
Multiple Input Multiple Output ◽  
Likelihood Estimation ◽  
Mimo Radar ◽  
Low Rank ◽  
Radar Target ◽  
Signal Power ◽  
Unknown Parameters ◽  
Multiple Input ◽  
Input Multiple Output

Motivated by the development of Multiple-Input Multiple-Output (MIMO) communication, MIMO radar has drawn considerable attention. While, to design of MIMO radar detector, transmitting signal power and noise are usually assumed known in advance, but in practice we may need to estimate the transmitting signal power and noise first. In this paper, we introduce MIMO radar target performance analysis with unknown parameters. First transmitting signal energy is estimated by Maximum likelihood Estimation(MLE) when multipath satisfy special diversity condition and multipath has low rank. Then the detector in the Neyman-Pearson is developed and analyzed with estimated parameters. The simulation results show that the performance with unknown parameters is approximate to the detector with known parameters. The method proposed in this paper can be used to design the MIMO radar detectors with unknown parameters.

scholarly journals Root-MUSIC Based Angle Estimation for MIMO Radar with Unknown Mutual Coupling

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianfeng Li ◽  
Xiaofei Zhang ◽  
Weiyang Chen
Keyword(s):  
Mutual Coupling ◽  
Estimation Error ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Lower Complexity ◽  
Toeplitz Structure

Direction of arrival (DOA) estimation problem for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied, and an algorithm for the DOA estimation based on root multiple signal classification (MUSIC) is proposed. Firstly, according to the Toeplitz structure of the mutual coupling matrix, output data of some specified sensors are selected to eliminate the influence of the mutual coupling. Then the reduced-dimension transformation is applied to make the computation burden lower as well as obtain a Vandermonde structure of the direction matrix. Finally, Root-MUSIC can be adopted for the angle estimation. 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 MUSIC-like algorithm. Furthermore, the proposed algorithm has lower complexity than them. The simulation results verify the effectiveness of the algorithm, and the theoretical estimation error of the algorithm is also derived.

scholarly journals DOA Estimation for Multiple Targets in MIMO Radar with Nonorthogonal Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zhenxin Cao ◽  
Peng Chen ◽  
Zhimin Chen ◽  
Yi Jin
Keyword(s):  
Correlation Matrix ◽  
Cross Correlation ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Estimation Performance ◽  
Multiple Input ◽  
Input Multiple Output

This paper addresses the direction of arrival (DOA) estimation problem in the colocated multiple-input multiple-output (MIMO) radar with nonorthogonal signals. The maximum number of targets that can be estimated is theoretically derived as rankRsN, where N denotes the number of receiving antennas and Rs is the cross-correlation matrix of the transmitted signals. Therefore, with the rank-deficient cross-correlation matrix, the maximum number that can be estimated is less than the radar with orthogonal signals. Then, a multiple signal classification- (MUSIC-) based algorithm is given for the nonorthogonal signals. Furthermore, the DOA estimation performance is also theoretically analyzed by the Carmér-Rao lower bound. Simulation results show that the nonorthogonality degrades the DOA estimation performance only in the scenario with the rank-deficient cross-correlation matrix.

scholarly journals Spatial Differencing Reconstruction Based Low-Angle DOA Estimation with MIMO Radar

2018 ◽  
Vol 36 (1) ◽  
pp. 182-188
Author(s):  
Qin Zhang ◽  
Linrang Zhang ◽  
Junpeng Shi ◽  
Yannian Zhou ◽  
Yaning Liu
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Diversity ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
Signal Model ◽  
Estimation Performance ◽  
Multiple Input ◽  
Input Multiple Output

Due to the multipath effect, the direction of arrival (DOA) estimation performance for low-angle targets decreases greatly. To overcome this problem, this paper proposes a spatial differencing reconstruction based DOA estimation algorithm by using the received signal model of multiple input multiple output (MIMO) radar. Combining with the spatial diversity of MIMO radar, the proposed method can first use the multipath echo power to select the best signal. Then, a spatial differencing based iterative scheme is developed to reduce the effect of additive noise, resulting in a better estimation performance for low-angle targets. Simulation results show that the proposed method has better advantages in suppressing noise and improving estimation accuracy.

scholarly journals DOA Estimation of a Novel Generalized Nested MIMO Radar with High Degrees of Freedom and Hole-Free Difference Coarray

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yule Zhang ◽  
Guoping Hu ◽  
Hao Zhou ◽  
Mingming Zhu ◽  
Fei Zhang
Keyword(s):  
Degrees Of Freedom ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Music Algorithm ◽  
Array Configuration ◽  
Matrix Reconstruction ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Array Aperture

A novel generalized nested multiple-input multiple-output (MIMO) radar for direction of arrival (DOA) estimation is proposed in this paper. The proposed structure utilizes the extended two-level nested array (ENA) as transmitter and receiver and adjusts the interelement spacing of the receiver with an expanding factor. By optimizing the array element configuration, we can obtain the best number of elements of the transmitter and receiver and the attainable degrees of freedom (DOF). Compared with the existing nested MIMO radar, the proposed MIMO array configuration not only has closed-form expressions for sensors’ positions and the number of maximum DOF, but also significantly improves the array aperture. It is verified that the sum-difference coarray (SDCA) of the proposed nested MIMO radar can get higher DOF without holes. MUSIC algorithm based on Toeplitz matrix reconstruction is employed to prove the rationality and superiority of the proposed MIMO structure.

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 Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2453 ◽  
Author(s):  
Guangyong Zheng ◽  
Siqi Na ◽  
Tianyao Huang ◽  
Lulu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Counter Measures ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Simulation Results ◽  
Cramer Rao Bound

Distributed multiple input multiple output (MIMO) radar has attracted much attention for its improved detection and estimation performance as well as enhanced electronic counter-counter measures (ECCM) ability. To protect the target from being detected and tracked by such radar, we consider a barrage jamming strategy towards a distributed MIMO. We first derive the Cramer–Rao bound (CRB) of target parameters estimation using a distributed MIMO under barrage jamming environments. We then set maximizing the CRB as the criterion for jamming resource allocation, aiming at degrading the accuracy of target parameters estimation. Due to the non-convexity of the CRB maximizing problem, particle swarm optimization is used to solve the problem. Simulation results demonstrate the advantages of the proposed strategy over traditional jamming methods.

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.

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