Signal-dependent interference reduction based on a new transmit covariance matrix and receive filter design for colocated MIMO radar

2019 ◽  
Vol 13 (7) ◽  
pp. 1275-1282 ◽  
Author(s):  
Mohammad Mahdi Feraidooni ◽  
Davood Gharavian ◽  
Sadjad Imani
Keyword(s):  
Covariance Matrix ◽  
Filter Design ◽  
Mimo Radar ◽  
Interference Reduction

scholarly journals SINR improvement based on joint design of transmit covariance matrix and receive filter design for colocated MIMO radar

2020 ◽  
Author(s):  
Sadjad Imani ◽  
Mohammad Mahdi Feraidooni ◽  
Davood Gharavian ◽  
Mohammad Mahdi Nayebi
Keyword(s):  
Covariance Matrix ◽  
Filter Design ◽  
Mimo Radar ◽  
Joint Design

Robust cognitive transmit waveform and receive filter design for airborne MIMO radar in signal-dependent clutter environment

2020 ◽  
Vol 101 ◽  
pp. 102709
Author(s):  
Qingsong Zhou ◽  
Zhihui Li ◽  
Junpeng Shi ◽  
Yunxiang Mao
Keyword(s):  
Filter Design ◽  
Mimo Radar

Transmit Signal Design in Colocated MIMO Radar Without Covariance Matrix Optimization

2017 ◽  
Vol 53 (5) ◽  
pp. 2178-2186 ◽  
Author(s):  
Sadjad Imani ◽  
Mohammad Mahdi Nayebi ◽  
Seyed Ali Ghorashi
Keyword(s):  
Covariance Matrix ◽  
Mimo Radar ◽  
Signal Design ◽  
Matrix Optimization

Space-time transmit code and receive filter design for colocated MIMO radar

2016 ◽  
Author(s):  
Xianxiang Yu ◽  
Guolong Cui ◽  
Lingjiang Kong ◽  
Vincenzo Carotenuto
Keyword(s):  
Filter Design ◽  
Mimo Radar ◽  
Space Time

Evaluation of Covariance Matrix in Distributed MIMO Radar

2012 ◽  
Vol 18 (7) ◽  
pp. 953-965
Author(s):  
Jurong Hu ◽  
Ning Cao ◽  
Hao Lu ◽  
Fei Wang
Keyword(s):  
Covariance Matrix ◽  
Mimo Radar

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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