Off-Grid Microwave Coincidence Imaging Based on Directional Grid Fission

Cao, Kaicheng; Cheng, Yongqiang; Liu, Kang; Wang, Hongqiang; Liu, Hongyan

doi:10.1109/lawp.2020.3037100

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…The benefit is that the initial grid interval can be larger than that of classic off-grid methods, so that the computational workload can be greatly saved. The learning process is generally completed based on Least Square Compressive Sensing [25] and the sparse Bayesian method [26,27]. In the fission process, the fission grids are usually determined by the results obtained in the learning process.…”

Section: Introductionmentioning

confidence: 99%

“…The signal magnitudes of the nearby grids on both sides of the fission location are compared to determine the fission direction in [26]. When the grid becomes dense, the magnitudes are affected by the mutual influence of the signals, while the values of off-grid parameter are less affected, so some research studies determined the fission grids by the off-grid parameter obtained in the learning process [27].…”

Section: Introductionmentioning

confidence: 99%

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Weighted sparse Bayesian method for direction of arrival estimation based on grid fission

Wei

2023

IET Signal Processing

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An off-grid weighted sparse Bayesian learning algorithm based on grid fission for direction of arrival estimation is proposed. The existing grid fission algorithms can use fewer grid points with variant intervals to estimate the true DOAs. However, their learning processes are based on the traditional sparse Bayesian algorithm, which only assigns the same prior distribution assumption to the signals on all grids, but ignores the difference of signal distribution of different grid points. It will result in inaccurate fission location and fission direction because of the insufficient resolution of the spatial spectrum, reducing the estimation accuracy. Moreover, the fission strategy will cost much computation time due to the increase of grid points. To solve these problems, the proposed algorithm utilises the orthogonality of signal subspace and noise subspace to design the weights for prior signal distribution assumption, making the peaks of spatial spectrum more pronounced and easy to distinguish, using more accurate estimated DOAs and offgrid parameter to determine the fission location and direction. In addition, the fission process deletes redundant grid points to simplify calculations. Compared with the existing grid fission algorithms, the proposed method has superior performance in estimation accuracy and computational time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%