基于最小熵的地基MIMO-SAR幅相校正GPU加速与嵌入式实现

doi:10.12305/j.issn.1001-506X.2026.08.10

Abstract

Abstract:

Addressing the channel amplitude and phase error issue in multiple input multiple output (MIMO) synthetic aperture radar (SAR), the minimum image entropy amplitude and phase correction algorithm serves as an effective solution. However, due to the large computational load and long estimation time, the algorithm is difficult to use for real-time correction. To this end, a graphics processing unit (GPU) implementation method for minimum entropy amplitude and phase correction is proposed. This method employs mixed-precision computation in data transmission and computation, ensuring algorithm accuracy while significantly enhancing computational efficiency. By leveraging asynchronous parallel stream processing technology, GPU resources are fully utilized to accelerate the algorithm, which is ultimately deployed on an embedded GPU platform. Through comparative analysis of measured data, test results on desktop GPU and embedded GPU demonstrate the accuracy and efficiency of the proposed method. The algorithm can effectively estimates amplitude and phase errors, achieving an acceleration ratio of up to 105%.

Key words: synthetic aperture radar (SAR), multiple input multiple output (MIMO) radar, channel amplitude-phase correction, minimum entropy algorithm, graphics processing unit (GPU) acceleration

CLC Number:

TN 911.7

Weijie XI, Tao LAI, Gaotian LIANG, Qingsong WANG, Haifeng HUANG. GPU acceleration and embedded implementation for amplitude-phase correction of ground-based MIMO-SAR based on minimum entropy[J]. Systems Engineering and Electronics, 2026, 48(8): 2615-2625.

Figures/Tables 13

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References 30

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算法核心步骤	乘法次数	加法次数
FFT/IFFT	$MN{\log _2}\;M$	$MN{\log _2}\;M$
误差补偿	$8MN$	$4MN$
熵计算	$3MN$	$2MN$
相位梯度	$5MN\left( {{{\log }_2}\;MN + 2} \right)$	$5MN\left( {{{\log }_2}\;MN + 1} \right)$
相位步长	$15MN\left( {{{\log }_2}\;MN + 2} \right)$	$5MN\left( {{{\log }_2}\;MN + 1} \right)$
幅度梯度	$10MN\left( {{{\log }_2}\;MN + 1} \right)$	$3MN\left( {3{{\log }_2}\;MN + 1} \right)$
幅度步长	$15MN\left( {{{\log }_2}\;MN + 2} \right)$	$15MN\left( {{{\log }_2}\;MN + 1} \right)$
搜索方向	$3M$	$3M$

处理平台	型号	核心配置
CPU	Intel（R） Core（TM） i9-12900H	2.5 G主频，32 G内存
GPU	GeForce RTX 4090	16384 个 CUDA 核心，24 G显存
GPU	Jetson Orin NX	1024 个 CUDA 核心，8 G显存

GPU平台和算法精度	幅度误差	相位误差
RTX 4090（双精度）	2.87×10⁻¹¹	3.30×10⁻¹¹
RTX 4090（混合精度）	1.06×10⁻³	7.52×10⁻³
Jetson NX（双精度）	2.99×10⁻¹¹	3.56×10⁻¹¹
Jetson NX（混合精度）	1.07×10⁻³	7.38×10⁻³

平台	方案	耗时/s	加速比
CPU	—	216.69	—
RTX 4090	常规	5.579	38.747
RTX 4090	本文算法	2.044	105.757
Jetson NX	常规	164.214	1.316
Jetson NX	本文算法	69.871	3.094

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GPU acceleration and embedded implementation for amplitude-phase correction of ground-based MIMO-SAR based on minimum entropy

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