New two-stage sparse representation for fast multi-target location in bistatic MIMO radar

2015 ◽  
Author(s):  
C.Y. Wang ◽  
L.L. Gong
Keyword(s):  
Sparse Representation ◽  
Target Location ◽  
Mimo Radar ◽  
Two Stage ◽  
Bistatic Mimo Radar

A Fast Target Location Method for Bistatic MIMO Radar with Spatial Colored Noise

2021 ◽  
Author(s):  
Weijia Yu ◽  
Yuanzhi Chen ◽  
Yuan Cheng ◽  
Jiali Cao ◽  
Jianhe Du
Keyword(s):  
Colored Noise ◽  
Target Location ◽  
Mimo Radar ◽  
Location Method ◽  
Bistatic Mimo Radar

Fast Angle Estimation Algorithm of Coherently Distributed Targets for Bistatic MIMO Radar

2011 ◽  
Vol 33 (7) ◽  
pp. 1684-1688
Author(s):  
Yi-duo Guo ◽  
Yong-shun Zhang ◽  
Lin-rang Zhang ◽  
Ning-ning Tong
Keyword(s):  
Estimation Algorithm ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Bistatic Mimo Radar

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.

An esprit-like algorithm for coherent angle estimation in bistatic MIMO radar

2015 ◽  
Author(s):  
Yali Wang Yali Wang ◽  
Zhiguo Liu Zhiguo Liu ◽  
Zhonghai Yin Zhonghai Yin ◽  
Qiang Sun Qiang Sun ◽  
Xiaolong Liang Xiaolong Liang
Keyword(s):  
Mimo Radar ◽  
Angle Estimation ◽  
Bistatic Mimo Radar

Analysis of Sparse Representation and Blind Source Separation

2004 ◽  
Vol 16 (6) ◽  
pp. 1193-1234 ◽  
Author(s):  
Yuanqing Li ◽  
Andrzej Cichocki ◽  
Shun-ichi Amari
Keyword(s):  
Sparse Representation ◽  
Blind Source Separation ◽  
Clustering Algorithm ◽  
Source Separation ◽  
Coefficient Matrix ◽  
Data Matrix ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Basis Matrix ◽  
Two Stage

In this letter, we analyze a two-stage cluster-then-l1-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l1 norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l1—norm solution and the l0—norm solution is also analyzed according to a probabilistic framework. If the obtained l1—norm solution is sufficiently sparse, then it is equal to the l0—norm solution with a high probability. Furthermore, the l1—norm solution is robust to noise, but the l0—norm solution is not, showing that the l1—norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed

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