随机样本选择的合成孔径雷达距离空变相位梯度自聚焦算法

doi:10.12305/j.issn.1001-506X.2023.12.12

Abstract

Abstract:

Aiming at the sample selection method in the range-dependent phase gradient autofocus (PGA) algorithm, a novel range-dependent PGA (RDPGA) algorithm based on stochastic sample selection is proposed in this paper. Different from the traditional algorithms, in which a fixed threshold is utilized to exclude a part of characteristic point samples, the proposed algorithm uses the samples' signal to clutter ratio (SCR) to construct a probability density function for sample selection. Samples are stochastically selected in each iteration according to the probability density function. Stochastic sample selection ensures the diversity of range cell samples, improving the estimation accuracy of RDPGA. In addition, the probability distribution ensures the high contribution of high-quality samples in the estimation of model parameters. It improves the robustness of convergence while promising a high efficiency. The actual measured data processing results show that the proposed algorithm has high estimation accuracy and robustness.

Key words: autofocus, phase gradient autofocus (PGA), stochastic sample selection

CLC Number:

TN975

Zhichao MENG, Lei ZHANG, Jingyue LU, Jun LI. A range-dependent phase gradient autofocus algorithm integrated stochastic sample selection for SAR imaging[J]. Systems Engineering and Electronics, 2023, 45(12): 3828-3835.

Figures/Tables 9

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References 24

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参数	数值	参数	数值
平均速度/(m·s^-1)	60.29	中心频率/GHz	9.6
平均高度/m	3 477	发射带宽/MHz	180
中心斜距/km	10.8	脉冲个数	4 096
斜视角/(°)	-1.25	距离点数	4 096

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A range-dependent phase gradient autofocus algorithm integrated stochastic sample selection for SAR imaging

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