Two-dimensional direction-of-arrival estimation method based on interpolation fitting for airborne conformal MIMO radar in a multipath environment

2021 ◽  
pp. 103374
Author(s):  
Yili Hu ◽  
Yongbo Zhao ◽  
Sheng Chen ◽  
Ben Niu
Keyword(s):  
Estimation Method ◽  
Direction Of Arrival ◽  
Mimo Radar ◽  
Direction Of Arrival Estimation ◽  
Two Dimensional ◽  
Multipath Environment

A Universal Two-Dimensional Direction of Arrival Estimation Method Without Parameter Match

2016 ◽  
Vol 16 (9) ◽  
pp. 3141-3146 ◽  
Author(s):  
Jiang Jia-jia ◽  
Duan Fa-jie ◽  
Wang Xian-Quan
Keyword(s):  
Estimation Method ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Two Dimensional

Sparse representation based two‐dimensional direction‐of‐arrival estimation method with L‐shaped array

2016 ◽  
Vol 10 (5) ◽  
pp. 976-982 ◽  
Author(s):  
ZengFei Cheng ◽  
Yongbo Zhao ◽  
YuTang Zhu ◽  
Penglang Shui ◽  
Hui Li
Keyword(s):  
Sparse Representation ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Two Dimensional

scholarly journals An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4403
Author(s):  
Ji Woong Paik ◽  
Joon-Ho Lee ◽  
Wooyoung Hong
Keyword(s):  
Covariance Matrix ◽  
Phased Array ◽  
Null Space ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Improve Performance ◽  
Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

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