Compressed sensing parallel factor analysis‐based joint angle and Doppler frequency estimation for monostatic multiple‐input–multiple‐output radar

2014 ◽  
Vol 8 (6) ◽  
pp. 597-606 ◽  
Author(s):  
Renzheng Cao ◽  
Xiaofei Zhang ◽  
Weiyang Chen
Keyword(s):  
Factor Analysis ◽  
Compressed Sensing ◽  
Joint Angle ◽  
Frequency Estimation ◽  
Multiple Input Multiple Output ◽  
Doppler Frequency ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Parallel Factor ◽  
Doppler Frequency Estimation

scholarly journals Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

2021 ◽  
Vol 13 (15) ◽  
pp. 2964
Author(s):  
Fangqing Wen ◽  
Junpeng Shi ◽  
Xinhai Wang ◽  
Lin Wang
Keyword(s):  
Numerical Study ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Phase Errors ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Angle Pair ◽  
Parallel Factor ◽  
Parafac Decomposition

Ideal transmitting and receiving (Tx/Rx) array response is always desirable in multiple-input multiple-output (MIMO) radar. In practice, nevertheless, Tx/Rx arrays may be susceptible to unknown gain-phase errors (GPE) and yield seriously decreased positioning accuracy. This paper focuses on the direction-of-departure (DOD) and direction-of-arrival (DOA) problem in bistatic MIMO radar with unknown gain-phase errors (GPE). A novel parallel factor (PARAFAC) estimator is proposed. The factor matrices containing DOD and DOA are firstly obtained via PARAFAC decomposition. One DOD-DOA pair estimation is then accomplished from the spectrum searching. Thereafter, the remainder DOD and DOA are achieved by the least squares technique with the previous estimated angle pair. The proposed estimator is analyzed in detail. It only requires one instrumental Tx/Rx sensor, and it outperforms the state-of-the-art algorithms. Numerical simulations verify the theoretical advantages.

scholarly journals Compressed Sensing Imaging with Compensation of Motion Errors for MIMO Radar

2021 ◽  
Vol 13 (23) ◽  
pp. 4909
Author(s):  
Haoran Li ◽  
Shuangxun Li ◽  
Zhi Li ◽  
Yongpeng Dai ◽  
Tian Jin
Keyword(s):  
Compressed Sensing ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Ground Vehicles ◽  
Motion Error ◽  
Phase Errors ◽  
Imaging Results ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Motion Error Compensation

Using a multiple-input-multiple-output (MIMO) radar for environment sensing is gaining more attention in unmanned ground vehicles (UGV). During the movement of the UGV, the position of MIMO array compared to the ideal imaging position will inevitably change. Although compressed sensing (CS) imaging can provide high resolution imaging results and reduce the complexity of the system, the inaccurate MIMO array elements position will lead to defocusing of imaging. In this paper, a method is proposed to realize MIMO array motion error compensation and sparse imaging simultaneously. It utilizes a block coordinate descent (BCD) method, which iteratively estimates the motion errors of the transmitting and receiving elements, as well as synchronously achieving the autofocus imaging. The method accurately estimates and compensates for the motion errors of the transmitters and receivers, rather than approximating them as phase errors in the data. The validity of the proposed method is verified by simulation and measured experiments in a smoky environment.

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