scholarly journals Sparse Recovery for Bistatic MIMO Radar Imaging in the Presence of Array Gain Uncertainties

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Jun Li ◽  
Shengqi Zhu ◽  
Xixi Chen ◽  
Li Lv ◽  
Guisheng Liao ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Multiple Input Multiple Output ◽  
Sparse Recovery ◽  
Mimo Radar ◽  
Range Cell ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Gain Errors ◽  
Array Gain ◽  
Receive Array

A sparse recovery based transmit-receive angle imaging scheme is proposed for bistatic multiple-input multiple-output (MIMO) radar. The redundancy of the transmit and receive angles in the same range cell is exploited to construct the sparse model. The imaging is then performed by compressive sensing method with consideration of both the transmit and receive array gain uncertainties. An additional constraint is imposed on the inverse of the transmit and receive array gain errors matrices to make the optimization problem of the CS solvable. The image of the targets can be reconstructed using small number of snapshots in the case of large array gain uncertainties. Simulation results confirm the effectiveness of the proposed scheme.

Array Grating Lobe Elimination of F-MIMO HF Radar with Colocated Antennas

2014 ◽  
Vol 556-562 ◽  
pp. 4510-4513
Author(s):  
Qiang Yang ◽  
Xian Mei Hou
Keyword(s):  
Multiple Input Multiple Output ◽  
Pso Algorithm ◽  
Mimo Radar ◽  
Hf Radar ◽  
Frequency Diversity ◽  
Grating Lobe ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Array Structure ◽  
Receive Array

Multiple-input multiple-output (MIMO) radar with frequency diversity (f-MIMO) is applied to HF radar. An array processing model of f-MIMO HF radar is developed. To eliminate the grating lobe of f-MIMO radar beamforming, two approaches are proposed. One is to apply particle swarm optimization (PSO) algorithm to select the optimal carrier frequency combination. Another is to extract array elements from the virtual receive array to get the optimal sparse array structure, and the simplified physical receive array structure is proposed. Simulation results demonstrate the effectiveness of the method proposed.

scholarly journals Cognitive MIMO Frequency Diverse Array Radar with High LPI Performance

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ling Huang ◽  
Kuandong Gao ◽  
Zhiming He ◽  
Jingye Cai
Keyword(s):  
Optimization Problem ◽  
Target Location ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Beam Pattern ◽  
Multiple Input ◽  
Convex Problem ◽  
Input Multiple Output ◽  
Frequency Diverse Array ◽  
Low Probability

Frequency diverse array (FDA) has its unique advantage in realizing low probability of intercept (LPI) technology for its dependent beam pattern. In this paper, we proposed a cognitive radar based on the frequency diverse array multiple-input multiple-output (MIMO). To implement LPI of FDA MIMO transmit signals, a scheme for array weighting design is proposed, which is to minimize the energy of the target location and maximize the energy of the receiver. This is based on the range dependent characteristics of the frequency diverse array transmit beam pattern. To realize the objective problem, the algorithm is proposed as follows: the second-order nonconvex optimization problem is converted into a convex problem and solved by the bisection method and convex optimization. To get the information of target, the FDA MIMO radar is proposed to estimate the target parameters. Simulation results show that the proposed approach is effective in decreasing the detection probability of radar with lossless detection performance of the receive signal.

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.

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