scholarly journals Sparse Bayesian Learning-Based Target Imaging and Parameter Estimation for Monostatic MIMO Radar Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 68545-68559 ◽  
Author(s):  
Amrita Mishra ◽  
Vini Gupta ◽  
Saumya Dwivedi ◽  
Aditya K. Jagannatham ◽  
Pramod K. Varshney
Keyword(s):  
Parameter Estimation ◽  
Bayesian Learning ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Radar Systems ◽  
Target Imaging

scholarly journals A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanwei Liu ◽  
Yongshun Zhang ◽  
Yiduo Guo ◽  
Qiang Wang ◽  
Yifeng Wu
Keyword(s):  
Sparse Representation ◽  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Time Spectrum ◽  
Weight Vector ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Multiple Measurement Vectors ◽  
Input Multiple Output ◽  
Temporally Correlated

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment.

scholarly journals Off-Grid DOA Estimation for Colocated MIMO Radar via Reduced-Complexity Sparse Bayesian Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 99907-99916 ◽  
Author(s):  
Tingting Liu ◽  
Fangqing Wen ◽  
Lei Zhang ◽  
Ke Wang
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Reduced Complexity

scholarly journals DOA estimation for monostatic MIMO radar using enhanced sparse Bayesian learning

2018 ◽  
Vol 2018 (5) ◽  
pp. 268-273 ◽  
Author(s):  
Fangqing Wen ◽  
Dongmei Huang ◽  
Ke Wang ◽  
Lei Zhang
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Sparse Bayesian Learning

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.

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