.png){kind=icon}
.png){kind=icon}
Return
Maximum Likelihood Sensor Array Calibration Using Non-Approximate Hession Matrix
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2021.3067556.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Unknown array non-ideal characteristics, including mutual coupling between array elements, channel gain/phase errors, and sensor position errors, will degrade the array performance of direction finding and beamforming. In this letter, we propose an improved maximum likelihood (ML) calibration algorithm to estimate all such non-ideal characteristics with lower computational complexity and faster convergence rate. First a modified formulation of the ML function is developed to avoid matrix inversion in each iteration. Then, a damped Newton formula with non-approximate Hession matrix is derived, which greatly improves the convergence rate. The numerical results demonstrate the effectiveness and efficiency of the proposed algorithm.
Keywords:
Maximum likelihood estimation
Calibration
Sensor arrays
Computational complexity
Convergence
Signal processing algorithms
Transmission line matrix methods
Channel gain
phase errors
maximum likelihood estimation
sensor array calibration
sensor position errors
mutual coupling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
No organization information available