Tensor-Based Angle and Array Gain-Phase Error Estimation Scheme in Bistatic MIMO Radar
Yuehao Guo,
Xianpeng Wang,
Liangtian Wan,
Mengxing Huang,
Chong Shen,
Kun Zhang,
Yongqin Yang
Affiliations
Yuehao Guo
State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou, China
State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou, China
Liangtian Wan
Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, China
Mengxing Huang
State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou, China
Chong Shen
State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou, China
Kun Zhang
State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou, China
Yongqin Yang
State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science and Technology, Hainan University, Haikou, China
This paper investigates the issue of angle and array gain-phase error estimation in multiple-input-multiple-output (MIMO) radar, and a tensor-based angle and gain-phase error estimation scheme is proposed. In our approach, the parallel factor (PARAFAC) decomposition is performed to estimate the transmit and receive direction matrices. Then the estimation of gain error can be obtained according to the relationship between the columns of direction matrices. After that, the linear feature of the phase in the additional well-calibrated array element is utilized to estimate the angles. Finally, by fully using the phase characteristics of all arrays, the phase error can be obtained. Our approach can remove the influence of error accumulation, and thus it has a superior angle and gain-phase error estimation performance, particularly under the condition of low signal-to-noise ratio (SNR). The numerical examples validate the superiority and effectiveness of the proposed scheme.
