Angular super-resolution using sparse multi-layer iterative algorithm for sparse arrays

doi:10.23919/JSEE.2026.000121

Abstract

Abstract:

This paper proposes a two-dimensional (2D) angular super-resolution framework for sparse arrays under the single snapshot condition. The azimuth-elevation 2D angular super-resolution model is established, which shows the relationship between the 2D angular super-resolution image and the signal. Using this model, the 2D angular super-resolution problem is transformed into the beyond linear optimization problem. In order to efficiently and accurately address this optimization problem, we propose the sparse multi-layer iterative (SMLI) algorithm based on beyond linear signal processing (BLiSP) theory. During the layered iterative process, the solution region is continuously narrowed. The constructed nonlinear weighting matrix and sparse constraint coefficient enhance the ability to differentiate between the effect of signal and noise in solving the beyond linear optimization problem. Additionally, the nonlinear weighting matrix can be adaptively updated during the solving process, ensuring high-performance angular super-resolution results under different signal-to-noise ratios (SNRs). Experiments confirm the proposed method’s effectiveness and robustness.

Key words: two-dimensional (2D) angular super-resolution, beyond linear optimization problem, sparse multi-layer iterative (SMLI), nonlinear weighting matrix

Min Xue, Mengdao Xing, Yuexin Gao, Yidi Wang, Yuan Jia, Jixiang Fu. Angular super-resolution using sparse multi-layer iterative algorithm for sparse arrays[J]. Journal of Systems Engineering and Electronics, 2026, 37(4): 1138-1150.

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Parameter	Value
Carrier frequency/GHz	25
Wavelength/m	0.012
Bandwidth/MHz	100
Sampling frequency/MHz	125
Ideal array configuration	10×10
Array sparsity/%	50%
Number of array elements	50
Elevation and azimuth angular resolution/(°)	11.5

Algorithm	Time
MUSIC	0.35
OMP	0.09
IAA	0.79
BPDN	3.24
Proposed algorithm	0.71