A method for joint angle and array gain‐phase error estimation in Bistatic multiple‐input multiple‐output non‐linear arrays

2014 ◽  
Vol 8 (2) ◽  
pp. 131-137 ◽  
Author(s):  
Li Jianfeng ◽  
Zhang Xiaofei
Keyword(s):  
Error Estimation ◽  
Joint Angle ◽  
Phase Error ◽  
Multiple Input Multiple Output ◽  
Linear Arrays ◽  
Multiple Input ◽  
Non Linear ◽  
Input Multiple Output ◽  
Array Gain

Reconstruction of multiple-input multiple-output non-linear differential equation models from the generalized frequency response function matrix

2000 ◽  
Vol 214 (1) ◽  
pp. 35-52
Author(s):  
S A Billings ◽  
A K Swain
Keyword(s):  
Differential Equation ◽  
Frequency Response ◽  
Multiple Input Multiple Output ◽  
Estimation Procedure ◽  
Response Data ◽  
Differential Equation Models ◽  
Multiple Input ◽  
Non Linear ◽  
Input Multiple Output ◽  
Linear Differential

A new algorithm is introduced to identify differential equation models for linear and non-linear multiple-input multiple-output systems from frequency response data using a weighted complex orthogonal estimator. The estimation procedure is progressive beginning with the estimation of the linear terms and then sequentially adding higher-order non-linear terms to build up the model. Simulated examples are included to demonstrate the performance of the new algorithm.

scholarly journals Multiple-input multiple-output symbol rate signal digital predistorter for non-linear multi-carrier satellite channels

2015 ◽  
Vol 9 (16) ◽  
pp. 2053-2059 ◽  
Author(s):  
Efrain Zenteno ◽  
Daniel Rönnow ◽  
M.R. Bhavani Shankar ◽  
Roberto Piazza ◽  
Björn Ottersten
Keyword(s):  
Multiple Input Multiple Output ◽  
Symbol Rate ◽  
Multiple Input ◽  
Non Linear ◽  
Input Multiple Output ◽  
Output Symbol ◽  
Satellite Channels ◽  
Digital Predistorter

scholarly journals Sparse Recovery for Bistatic MIMO Radar Imaging in the Presence of Array Gain Uncertainties

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Jun Li ◽  
Shengqi Zhu ◽  
Xixi Chen ◽  
Li Lv ◽  
Guisheng Liao ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Multiple Input Multiple Output ◽  
Sparse Recovery ◽  
Mimo Radar ◽  
Range Cell ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Gain Errors ◽  
Array Gain ◽  
Receive Array

A sparse recovery based transmit-receive angle imaging scheme is proposed for bistatic multiple-input multiple-output (MIMO) radar. The redundancy of the transmit and receive angles in the same range cell is exploited to construct the sparse model. The imaging is then performed by compressive sensing method with consideration of both the transmit and receive array gain uncertainties. An additional constraint is imposed on the inverse of the transmit and receive array gain errors matrices to make the optimization problem of the CS solvable. The image of the targets can be reconstructed using small number of snapshots in the case of large array gain uncertainties. Simulation results confirm the effectiveness of the proposed scheme.

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