Multi-SVD based subspace estimation to improve angle estimation accuracy in bistatic MIMO radar

2013 ◽  
Vol 93 (7) ◽  
pp. 2003-2009 ◽  
Author(s):  
Yuanbing Cheng ◽  
Rusheng Yu ◽  
Hong Gu ◽  
Weimin Su
Keyword(s):  
Mimo Radar ◽  
Estimation Accuracy ◽  
Angle Estimation ◽  
Bistatic Mimo Radar ◽  
Subspace Estimation

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

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

scholarly journals Computationally Efficient Compressed Sensing-Based Method via FG Nyström in Bistatic MIMO Radar with Array Gain-Phase Error Effect

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jurong Hu ◽  
Evans Baidoo ◽  
Lei Zhan ◽  
Ying Tian
Keyword(s):  
Matching Pursuit ◽  
Phase Error ◽  
Mimo Radar ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Computationally Efficient ◽  
Angle Estimation ◽  
Bistatic Mimo Radar ◽  
Phase Errors ◽  
Sparse Signal Representation

In this paper, a robust angle estimator for uncorrelated targets that employs a compressed sense (CS) scheme following a fast greedy (FG) computation is proposed to achieve improved computational efficiency and performance for the bistatic MIMO radar with unknown gain-phase errors. The algorithm initially avoids the wholly computation of the received signal by compiling a lower approximation through a greedy Nyström approach. Then, the approximated signal is transformed into a sparse signal representation where the sparsity of the target is exploited in the spatial domain. Finally, a CS method, Simultaneous Orthogonal Matching Pursuit with an inherent gradient descent method, is utilized to reconstruct the signal and estimate the angles and the unknown gain-phase errors. The proposed algorithm, aside achieving closed-form resolution for automatically paired angle estimation, offers attractive computational competitiveness, specifically in large array scenarios. Additionally, the analyses of the computational complexity and the Cramér–Rao bounds for angle estimation are derived theoretically. Numerical experiments demonstrate the improvement and effectiveness of the proposed method against existing methods.

scholarly journals Time Reversal Linearly Constrained Minimum Power Algorithm for Direction of Arrival Estimation in Diffuse Multipath Environments

2020 ◽  
Vol 12 (20) ◽  
pp. 3344
Author(s):  
Chao Xiong ◽  
Chongyi Fan ◽  
Xiaotao Huang
Keyword(s):  
Covariance Matrix ◽  
Time Reversal ◽  
Direction Of Arrival ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
Angle Estimation ◽  
Minimum Power ◽  
Sensing Applications ◽  
Linearly Constrained ◽  
Multipath Signals

Direction of arrival (DOA) estimation in diffuse multipath environments is a challenge for ground-based radar remote sensing applications, which has significant value in military fields, such as air defense surveillance. However, radar received echo usually contains various multipath signals caused by the reflection of complex ground or sea surface. With the introduction of multipath signals, traditional algorithms’ performance on angle estimation decreases severely. In response to this problem, the letter proposes a new time reversal (TR) algorithm used for multiple-input multiple-output (MIMO) radar angle estimation. First, the algorithm reconstructs a TR covariance matrix by multiplexing the data’s rows and columns, increasing the estimation accuracy of the TR covariance matrix. Besides, the letter applies a linearly constrained minimum power (LCMP) constraint to suppress diffuse multipath signals according to the prior knowledge of environments. Simulation results examine the improvement of estimation accuracy by the proposed algorithm, also verify the superiority of the proposed algorithm in different multipath scenarios. What’s more, the algorithm has broader applicability due to avoiding the difficulties of removing the coherence and estimating multipath number in practice.

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