Phased Arrays for Multiple-Input Multiple-Output (MIMO) Radar: Tutorial Session Overview 22 April 2019

2019 ◽  
Author(s):  
V. Mecca ◽  
F. Robey ◽  
D. Rabideau
Keyword(s):  
Phased Arrays ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Tutorial Session ◽  
Multiple Input ◽  
Input Multiple Output

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.

Oil-Spills Detection in MIMO-SAR Radar Images by High Order Statistics

2013 ◽  
Vol 443 ◽  
pp. 649-652
Author(s):  
Yan Ling Luo
Keyword(s):  
Oil Spills ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Radar Signal ◽  
Future Research ◽  
Signal Model ◽  
Radar Images ◽  
Multiple Input ◽  
Input Multiple Output ◽  
High Order Statistics

MIMO radar (Multiple input multiple output radar) is a hot topic which gets lots of attention from researchers all around the world recently. It can achieve better detection performance than conventional phased radar. In this paper, the MIMO radar signal model is studied, and then the concept of MIMO radar is applied into SAR. The technique is employed to detect the oil spill in sea. At last, some conclusion is drawn. And some item for future research in presented also.

scholarly journals Multitarget Direct Localization Using Block Sparse Bayesian Learning in Distributed MIMO Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Bin Sun ◽  
Haowen Chen ◽  
Xizhang Wei ◽  
Xiang Li
Keyword(s):  
Sparse Representation ◽  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Localization Accuracy ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Direct Localization ◽  
Correct Target

The target localization in distributed multiple-input multiple-output (MIMO) radar is a problem of great interest. This problem becomes more complicated for the case of multitarget where the measurement should be associated with the correct target. Sparse representation has been demonstrated to be a powerful framework for direct position determination (DPD) algorithms which avoid the association process. In this paper, we explore a novel sparsity-based DPD method to locate multiple targets using distributed MIMO radar. Since the sparse representation coefficients exhibit block sparsity, we use a block sparse Bayesian learning (BSBL) method to estimate the locations of multitarget, which has many advantages over existing block sparse model based algorithms. Experimental results illustrate that DPD using BSBL can achieve better localization accuracy and higher robustness against block coherence and compressed sensing (CS) than popular algorithms in most cases especially for dense targets case.

The Data Fusion for MIMO Radar

2010 ◽  
Vol 121-122 ◽  
pp. 627-632 ◽  
Author(s):  
Jian Kui Zeng ◽  
Zi Ming Dong
Keyword(s):  
Data Fusion ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Fusion Center ◽  
Fusion Technique ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Radar Technique

Multiple Input Multiple Output (MIMO) radar is a new emerging radar technique developed recently. In this paper, the principle of MIMO Radar based on transmitting diversity is described and then the data fusion technique for MIMO radar is presented. In this method, the detection result of each detector of MIMO radar is integrated in data fusion center, a final detection result is get which includes all the information of each detector result.

Multi-Target Direction Measurement of Bistatic MIMO Radar

2014 ◽  
Vol 556-562 ◽  
pp. 2797-2801
Author(s):  
Jing Fang Wang
Keyword(s):  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Radar Signal ◽  
Great Success ◽  
Two Dimensional ◽  
Signal Model ◽  
Bistatic Mimo Radar ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Technology

Multiple-input multiple output (MIMO) radar has been widespread concern in the domestic and foreign researchers. Bistatic radar draws on the great success of MIMO technology in the communications field, and it has many advantages over conventional radar. The direction angles estimations of bistatic MIMO radar are researched. To contrast traditional radar DOA estimates, the direction vector of the bistatic MIMO radar is the Knonecker plot of the emission vector and reception vector, that two-dimensional direction angles is estimated. To solve this problem, the principle of bistatic MIMO radar signal model is in-depthly researched.By proposing Capon dimensionality reduction method, the two-dimensional directions of the dual-based MIMO radar are estimated, and computer simulation is to verify the effectiveness of the method.

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.

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