AI Article Synopsis
- Traditional calibration methods struggle due to direction-dependent mutual coupling (MC), which arises when assuming omnidirectional elements isn't effective.
- This paper introduces a new self-calibration technique using time-frequency distributions (TFDs) to address this issue by calculating space-time-frequency distributions (STFDs) from received signals.
- The proposed method involves estimating steering vectors and noise subspaces, forming linear equations, and solving them using a least squares approach, resulting in improved performance in self-calibration over existing algorithms.
Article Abstract
In practical applications, the assumption of omnidirectional elements is not effective in general, which leads to the direction-dependent mutual coupling (MC). Under this condition, the performance of traditional calibration algorithms suffers. This paper proposes a new self-calibration method based on the time-frequency distributions (TFDs) in the presence of direction-dependent MC. Firstly, the time-frequency (TF) transformation is used to calculate the space-time-frequency distributions (STFDs) matrix of received signals. After that, the estimated steering vector and corresponding noise subspace are estimated by the steps of noise removing, single-source TF points extracting and clustering. Then according to the transformation relationship between the MC coefficients, steering vector and MC matrix, we deduce a set of linear equations. Finally, with two-step alternating iteration, the equations are solved by least square method in order to estimate DOA and MC coefficients. Simulations results show that the proposed algorithm can achieve direction-dependent MC self-calibration and outperforms the existing algorithms.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412500 | PMC |
| http://dx.doi.org/10.3390/s19040978 | DOI Listing |
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